Physical Sciences Forum

8 pages, 1811 KiB

Open AccessProceeding Paper

Comparison of Different Far-UVC Sources with Regards to Intensity Stability, Estimated Antimicrobial Efficiency and Potential Human Hazard in Comparison to a Conventional UVC Lamp

by Ben Sicks, Florian Maiss, Christian Lingenfelder, Cornelia Wiegand and Martin Hessling

Phys. Sci. Forum 2024, 10(1), 1; https://doi.org/10.3390/psf2024010001 - 19 Nov 2024

Abstract

The recently much noticed Far-UVC spectral range offers the possibility of inactivating pathogens without necessarily posing a major danger to humans. Unfortunately, there are various Far-UVC sources that differ significantly in their longer wavelength UVC emission and, subsequently, in their risk potential. Therefore, [...] Read more.

The recently much noticed Far-UVC spectral range offers the possibility of inactivating pathogens without necessarily posing a major danger to humans. Unfortunately, there are various Far-UVC sources that differ significantly in their longer wavelength UVC emission and, subsequently, in their risk potential. Therefore, a simple assessment method for Far-UVC sources is presented here. In addition, the temporal intensity stability of Far-UVC sources was examined in order to reduce possible errors in irradiation measurements. For this purpose, four Far-UVC sources and a conventional Hg UVC lamp were each spectrally measured for about 100 h and mathematically evaluated for their antimicrobial effect and hazard potential using available standard data. The two filtered KrCl lamps were found to be most stable after a warm-up time of 30 min. With regard to the antimicrobial effect, the radiation efficiencies of all examined (Far-) UVC sources were more or less similar. However, the calculated differences in the potential human hazard to eyes and skin were more than one order of magnitude. The two filtered KrCl lamps were the safest, followed by an unfiltered KrCl lamp, a Far-UVC LED and, finally, the Hg lamp. When experimenting with these Far-UVC radiation sources, the irradiance should be checked more than once. If UVC radiation is to be or could be applied in the presence of humans, filtered KrCl lamps are a much better choice than any other available Far-UVC sources. Full article

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7 pages, 967 KiB

Open AccessProceeding Paper

Nested Sampling for Detection and Localization of Sound Sources Using a Spherical Microphone Array

by Ning Xiang and Tomislav Jasa

Phys. Sci. Forum 2023, 9(1), 26; https://doi.org/10.3390/psf2023009026 - 20 May 2024

Abstract

Since its inception in 2004, nested sampling has been used in acoustics applications. This work applies nested sampling within a Bayesian framework to the detection and localization of sound sources using a spherical microphone array. Beyond an existing work, this source localization task [...] Read more.

Since its inception in 2004, nested sampling has been used in acoustics applications. This work applies nested sampling within a Bayesian framework to the detection and localization of sound sources using a spherical microphone array. Beyond an existing work, this source localization task relies on spherical harmonics to establish parametric models that distinguish the background sound environment from the presence of sound sources. Upon a positive detection, the parametric models are also involved to estimate an unknown number of potentially multiple sound sources. For the purpose of source detection, a no-source scenario needs to be considered in addition to the presence of at least one sound source. Specifically, the spherical microphone array senses the sound environment. The acoustic data are analyzed via spherical Fourier transforms using a Bayesian model comparison of two different models accounting for the absence and presence of sound sources for the source detection. Upon a positive detection, potentially multiple source models are involved to analyze direction of arrivals (DoAs) using Bayesian model selection and parameter estimation for the sound source enumeration and localization. These are two levels (enumeration and localization) of inferential estimations necessary to correctly localize potentially multiple sound sources. This paper discusses an efficient implementation of the nested sampling algorithm applied to the sound source detection and localization within the Bayesian framework. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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6 pages, 669 KiB

Open AccessProceeding Paper

Manifold-Based Geometric Exploration of Optimization Solutions

by Guillaume Lebonvallet, Faicel Hnaien and Hichem Snoussi

Phys. Sci. Forum 2023, 9(1), 25; https://doi.org/10.3390/psf2023009025 - 16 May 2024

Abstract

This work introduces a new method for the exploration of solutions space in complex problems. This method consists of the build of a latent space which gives a new encoding of the solution space. We map the objective function on the latent space [...] Read more.

This work introduces a new method for the exploration of solutions space in complex problems. This method consists of the build of a latent space which gives a new encoding of the solution space. We map the objective function on the latent space using a manifold, i.e., a mathematical object defined by an equations system. The latent space is built with some knowledge of the objective function to make the mapping of the manifold easier. In this work, we introduce a new encoding for the Travelling Salesman Problem (TSP) and we give a new method for finding the optimal round. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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8 pages, 2751 KiB

Open AccessProceeding Paper

NuMI Beam Monitoring Simulation and Data Analysis

by Yiding Yu, Thomas Joseph Carroll, Sudeshna Ganguly, Karol Lang, Eduardo Ossorio, Pavel Snopok, Jennifer Thomas, Don Athula Wickremasinghe and Katsuya Yonehara

Phys. Sci. Forum 2023, 8(1), 73; https://doi.org/10.3390/psf2023008073 - 22 Apr 2024

Abstract

Following the decommissioning of the Main Injector Neutrino Oscillation Search (MINOS) experiment, muon and hadron monitors have emerged as vital diagnostic tools for the NuMI Off-axis

ν_{μ}

Appearance (NOvA) experiment at Fermilab. These tools are crucial for overseeing the Neutrinos at the [...] Read more.

Following the decommissioning of the Main Injector Neutrino Oscillation Search (MINOS) experiment, muon and hadron monitors have emerged as vital diagnostic tools for the NuMI Off-axis

ν_{μ}

Appearance (NOvA) experiment at Fermilab. These tools are crucial for overseeing the Neutrinos at the Main Injector (NuMI) beam. This study endeavors to ensure the monitor signal quality and to correlate them with the Neutrino beam profile. Leveraging muon monitor simulations, we systematically explore the monitor responses to variations in proton-beam and lattice parameters. Through the amalgamation of individual pixel data from muon monitors, pattern-recognition algorithms, simulations, and measured data, we devise machine-learning-based models to predict muon monitor responses and Neutrino flux. Full article

(This article belongs to the Proceedings of The 23rd International Workshop on Neutrinos from Accelerators)

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7 pages, 269 KiB

Open AccessProceeding Paper

Analysis of Ecological Networks: Linear Inverse Modeling and Information Theory Tools

by Valérie Girardin, Théo Grente, Nathalie Niquil and Philippe Regnault

Phys. Sci. Forum 2023, 9(1), 24; https://doi.org/10.3390/psf2023009024 - 20 Feb 2024

Abstract

In marine ecology, the most studied interactions are trophic and are in networks called food webs. Trophic modeling is mainly based on weighted networks, where each weighted edge corresponds to a flow of organic matter between two trophic compartments, containing individuals of similar [...] Read more.

In marine ecology, the most studied interactions are trophic and are in networks called food webs. Trophic modeling is mainly based on weighted networks, where each weighted edge corresponds to a flow of organic matter between two trophic compartments, containing individuals of similar feeding behaviors and metabolisms and with the same predators. To take into account the unknown flow values within food webs, a class of methods called Linear Inverse Modeling was developed. The total linear constraints, equations and inequations defines a multidimensional convex-bounded polyhedron, called a polytope, within which lie all realistic solutions to the problem. To describe this polytope, a possible method is to calculate a representative sample of solutions by using the Monte Carlo Markov Chain approach. In order to extract a unique solution from the simulated sample, several goal (cost) functions—also called Ecological Network Analysis indices—have been introduced in the literature as criteria of fitness to the ecosystems. These tools are all related to information theory. Here we introduce new functions that potentially provide a better fit of the estimated model to the ecosystem. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

6 pages, 515 KiB

Open AccessProceeding Paper

Development of a Clock Generation and Time Distribution System for Hyper-Kamiokande

by Lucile Mellet, Mathieu Guigue, Boris Popov, Stefano Russo and Vincent Voisin

Phys. Sci. Forum 2023, 8(1), 72; https://doi.org/10.3390/psf2023008072 - 18 Jan 2024

Abstract

The construction of the next-generation water Cherenkov detector Hyper-Kamiokande (HK) has started. It will have about a ten times larger fiducial volume compared to the existing Super-Kamiokande detector, as well as increased detection performances. The data collection process is planned from 2027 onwards. [...] Read more.

The construction of the next-generation water Cherenkov detector Hyper-Kamiokande (HK) has started. It will have about a ten times larger fiducial volume compared to the existing Super-Kamiokande detector, as well as increased detection performances. The data collection process is planned from 2027 onwards. Time stability is crucial, as detecting physics events relies on reconstructing Cherenkov rings based on the coincidence between the photomultipliers. The above requires a distributed clock jitter at each endpoint that is smaller than 100 ps. In addition, since this detector will be mainly used to detect neutrinos produced by the J-PARC accelerator in Tokai, each event needs to be timed-tagged with a precision better than 100 ns, with respect to UTC, in order to be associated with a proton spill from J-PARC or the events observed in other detectors for multi-messenger astronomy. The HK collaboration is in an R&D phase and several groups are working in parallel for the electronics system. This proceeding will present the studies performed at LPNHE (Paris) related to a novel design for the time synchronization system in Kamioka with respect to the previous KamiokaNDE series of experiments. We will discuss the clock generation, including the connection scheme between the GNSS receiver (Septentrio) and the atomic clock (free-running Rubidium), the precise calibration of the atomic clock and algorithms to account for errors on satellites orbits, the redundancy of the system, and a two-stage distribution system that sends the clock and various timing-sensitive information to each front-end electronics module, using a custom protocol. Full article

(This article belongs to the Proceedings of The 23rd International Workshop on Neutrinos from Accelerators)

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10 pages, 743 KiB

Open AccessProceeding Paper

Preconditioned Monte Carlo for Gradient-Free Bayesian Inference in the Physical Sciences

by Minas Karamanis and Uroš Seljak

Phys. Sci. Forum 2023, 9(1), 23; https://doi.org/10.3390/psf2023009023 - 9 Jan 2024

Cited by 1

Abstract

We present preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference in complex probability distributions. PMC incorporates a normalizing flow (NF) and an adaptive Sequential Monte Carlo (SMC) scheme, along with a novel past resampling scheme to boost the number [...] Read more.

We present preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference in complex probability distributions. PMC incorporates a normalizing flow (NF) and an adaptive Sequential Monte Carlo (SMC) scheme, along with a novel past resampling scheme to boost the number of propagated particles without extra computational costs. Additionally, we utilize preconditioned Crank–Nicolson updates, enabling PMC to scale to higher dimensions without the gradient of target distribution. The efficacy of PMC in producing samples, estimating model evidence, and executing robust inference is showcased through two challenging case studies, highlighting its superior performance compared to conventional methods. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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8 pages, 2089 KiB

Open AccessProceeding Paper

Nested Sampling—The Idea

by John Skilling

Phys. Sci. Forum 2023, 9(1), 22; https://doi.org/10.3390/psf2023009022 - 8 Jan 2024

Abstract

We seek to add up

Q = \int f d X

over unit volume in arbitrary dimension. Nested sampling locates the bulk of Q by geometrical compression, using a Monte Carlo ensemble constrained within a progressively more restrictive lower limit [...] Read more.

We seek to add up

Q = \int f d X

over unit volume in arbitrary dimension. Nested sampling locates the bulk of Q by geometrical compression, using a Monte Carlo ensemble constrained within a progressively more restrictive lower limit

f \leq f^{*}

. This domain is divided into a core

f > f^{*}

and a shell

f = f^{*}

, with the core kept adequately populated. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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9 pages, 1337 KiB

Open AccessProceeding Paper

Flow Annealed Kalman Inversion for Gradient-Free Inference in Bayesian Inverse Problems

by Richard D. P. Grumitt, Minas Karamanis and Uroš Seljak

Phys. Sci. Forum 2023, 9(1), 21; https://doi.org/10.3390/psf2023009021 - 4 Jan 2024

Abstract

For many scientific inverse problems, we are required to evaluate an expensive forward model. Moreover, the model is often given in such a form that it is unrealistic to access its gradients. In such a scenario, standard Markov Chain Monte Carlo algorithms quickly [...] Read more.

For many scientific inverse problems, we are required to evaluate an expensive forward model. Moreover, the model is often given in such a form that it is unrealistic to access its gradients. In such a scenario, standard Markov Chain Monte Carlo algorithms quickly become impractical, requiring a large number of serial model evaluations to converge on the target distribution. In this paper, we introduce Flow Annealed Kalman Inversion (FAKI). This is a generalization of Ensemble Kalman Inversion (EKI) where we embed the Kalman filter updates in a temperature annealing scheme and use normalizing flows (NFs) to map the intermediate measures corresponding to each temperature level to the standard Gaussian. Thus, we relax the Gaussian ansatz for the intermediate measures used in standard EKI, allowing us to achieve higher-fidelity approximations to non-Gaussian targets. We demonstrate the performance of FAKI on two numerical benchmarks, showing dramatic improvements over standard EKI in terms of accuracy whilst accelerating its already rapid convergence properties (typically in

O (10)

steps). Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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8 pages, 2635 KiB

Open AccessProceeding Paper

Knowledge-Based Image Analysis: Bayesian Evidences Enable the Comparison of Different Image Segmentation Pipelines

by Mats Leif Moskopp, Andreas Deussen and Peter Dieterich

Phys. Sci. Forum 2023, 9(1), 20; https://doi.org/10.3390/psf2023009020 - 4 Jan 2024

Abstract

The analysis and evaluation of microscopic image data is essential in life sciences. Increasing temporal and spatial digital image resolution and the size of data sets promotes the necessity of automated image analysis. Previously, our group proposed a Bayesian formalism that allows for [...] Read more.

The analysis and evaluation of microscopic image data is essential in life sciences. Increasing temporal and spatial digital image resolution and the size of data sets promotes the necessity of automated image analysis. Previously, our group proposed a Bayesian formalism that allows for converting the experimenter’s knowledge, in the form of a manually segmented image, into machine-readable probability distributions of the parameters of an image segmentation pipeline. This approach preserved the level of detail provided by expert knowledge and interobserver variability and has proven robust to a variety of recording qualities and imaging artifacts. In the present work, Bayesian evidences were used to compare different image processing pipelines. As an illustrative example, a microscopic phase contrast image of a wound healing assay and its manual segmentation by the experimenter (ground truth) are used. Six different variations of image segmentation pipelines are introduced. The aim was to find the image segmentation pipeline that is best to automatically segment the input image given the expert knowledge with respect to the principle of Occam’s razor to avoid unnecessary complexity and computation. While none of the introduced image segmentation pipelines fail completely, it is illustrated that assessing the quality of the image segmentation with the naked eye is not feasible. Bayesian evidence (and the intrinsically estimated uncertainty

σ

of the image segmentation) is used to choose the best image processing pipeline for the given image. This work illustrates a proof of principle and is extendable to a diverse range of image segmentation problems. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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9 pages, 982 KiB

Open AccessProceeding Paper

Inferring Evidence from Nested Sampling Data via Information Field Theory

by Margret Westerkamp, Jakob Roth, Philipp Frank, Will Handley and Torsten Enßlin

Phys. Sci. Forum 2023, 9(1), 19; https://doi.org/10.3390/psf2023009019 - 13 Dec 2023

Cited by 1

Abstract

Nested sampling provides an estimate of the evidence of a Bayesian inference problem via probing the likelihood as a function of the enclosed prior volume. However, the lack of precise values of the enclosed prior mass of the samples introduces probing noise, which [...] Read more.

Nested sampling provides an estimate of the evidence of a Bayesian inference problem via probing the likelihood as a function of the enclosed prior volume. However, the lack of precise values of the enclosed prior mass of the samples introduces probing noise, which can hamper high-accuracy determinations of the evidence values as estimated from the likelihood-prior-volume function. We introduce an approach based on information field theory, a framework for non-parametric function reconstruction from data, that infers the likelihood-prior-volume function by exploiting its smoothness and thereby aims to improve the evidence calculation. Our method provides posterior samples of the likelihood-prior-volume function that translate into a quantification of the remaining sampling noise for the evidence estimate, or for any other quantity derived from the likelihood-prior-volume function. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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10 pages, 7936 KiB

Open AccessProceeding Paper

A BRAIN Study to Tackle Image Analysis with Artificial Intelligence in the ALMA 2030 Era

by Fabrizia Guglielmetti, Michele Delli Veneri, Ivano Baronchelli, Carmen Blanco, Andrea Dosi, Torsten Enßlin, Vishal Johnson, Giuseppe Longo, Jakob Roth, Felix Stoehr, Łukasz Tychoniec and Eric Villard

Phys. Sci. Forum 2023, 9(1), 18; https://doi.org/10.3390/psf2023009018 - 13 Dec 2023

Abstract

An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis, employing astrostatistics and astroinformatics. These emerging fields of research offer interdisciplinary approaches at the intersection of observational astronomy, statistics, algorithm development, and data science. In this [...] Read more.

An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis, employing astrostatistics and astroinformatics. These emerging fields of research offer interdisciplinary approaches at the intersection of observational astronomy, statistics, algorithm development, and data science. In this study, we provide evidence of the benefits of employing these approaches to ALMA imaging for operational and scientific purposes. We show the potential of two techniques, RESOLVE and DeepFocus, applied to ALMA-calibrated science data. Significant advantages are provided with the prospect to improve the quality and completeness of the data products stored in the science archive and the overall processing time for operations. Both approaches evidence the logical pathway to address the incoming revolution in data rates dictated by the planned electronic upgrades. Moreover, we bring to the community additional products through a new package, ALMASim, to promote advancements in these fields, providing a refined ALMA simulator usable by a large community for training and testing new algorithms. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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5 pages, 255 KiB

Open AccessProceeding Paper

Snowballing Nested Sampling

by Johannes Buchner

Phys. Sci. Forum 2023, 9(1), 17; https://doi.org/10.3390/psf2023009017 - 6 Dec 2023

Abstract

A new way to run nested sampling, combined with realistic MCMC proposals to generate new live points, is presented. Nested sampling is run with a fixed number of MCMC steps. Subsequently, snowballing nested sampling extends the run to more and more live points. [...] Read more.

A new way to run nested sampling, combined with realistic MCMC proposals to generate new live points, is presented. Nested sampling is run with a fixed number of MCMC steps. Subsequently, snowballing nested sampling extends the run to more and more live points. This stabilizes the MCMC proposal of later MCMC proposals, and leads to pleasant properties, including that the number of live points and number of MCMC steps do not have to be calibrated, that the evidence and posterior approximation improve as more compute is added and can be diagnosed with convergence diagnostics from the MCMC community. Snowballing nested sampling converges to a “perfect” nested sampling run with an infinite number of MCMC steps. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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9 pages, 285 KiB

Open AccessProceeding Paper

Quantum Measurement and Objective Classical Reality

by Vishal Johnson, Philipp Frank and Torsten Enßlin

Phys. Sci. Forum 2023, 9(1), 16; https://doi.org/10.3390/psf2023009016 - 6 Dec 2023

Abstract

We explore quantum measurement in the context of Everettian unitary quantum mechanics and construct an explicit unitary measurement procedure. We propose the existence of prior correlated states that enable this procedure to work and therefore argue that correlation is a resource that is [...] Read more.

We explore quantum measurement in the context of Everettian unitary quantum mechanics and construct an explicit unitary measurement procedure. We propose the existence of prior correlated states that enable this procedure to work and therefore argue that correlation is a resource that is consumed when measurements take place. It is also argued that a network of such measurements establishes a stable objective classical reality. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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4 pages, 15553 KiB

Open AccessProceeding Paper

Three-Dimensional Visualization of Astronomy Data Using Virtual Reality

by Gilles Ferrand

Phys. Sci. Forum 2023, 8(1), 71; https://doi.org/10.3390/psf2023008071 - 5 Dec 2023

Abstract

Visualization is an essential part of research, both to explore one’s data and to communicate one’s findings with others. Many data products in astronomy come in the form of multi-dimensional cubes, and since our brains are tuned for recognition in a 3D world, [...] Read more.

Visualization is an essential part of research, both to explore one’s data and to communicate one’s findings with others. Many data products in astronomy come in the form of multi-dimensional cubes, and since our brains are tuned for recognition in a 3D world, we ought to display and manipulate these in 3D space. This is possible with virtual reality (VR) devices. Drawing from our experiments developing immersive and interactive 3D experiences from actual science data at the Astrophysical Big Bang Laboratory (ABBL), this paper gives an overview of the opportunities and challenges that are awaiting astrophysicists in the burgeoning VR space. It covers both software and hardware matters, as well as practical aspects for successful delivery to the public. Full article

(This article belongs to the Proceedings of The 23rd International Workshop on Neutrinos from Accelerators)

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8 pages, 2328 KiB

Open AccessProceeding Paper

Searches for Dark Matter in the Galactic Halo and Extragalactic Sources with IceCube

by Minjin Jeong

Phys. Sci. Forum 2023, 8(1), 70; https://doi.org/10.3390/psf2023008070 - 5 Dec 2023

Abstract

Although there is overwhelming evidence for the existence of dark matter, the nature of dark matter remains largely unknown. Neutrino telescopes are powerful tools to search indirectly for dark matter, through the detection of neutrinos produced during dark matter decay or annihilation processes. [...] Read more.

Although there is overwhelming evidence for the existence of dark matter, the nature of dark matter remains largely unknown. Neutrino telescopes are powerful tools to search indirectly for dark matter, through the detection of neutrinos produced during dark matter decay or annihilation processes. The IceCube Neutrino Observatory is a cubic-kilometer-scale neutrino telescope located under 1.5 km of ice near the Amundsen-Scott South Pole Station. Various dark matter searches were performed with IceCube over the last decade, providing strong constraints on dark matter models. In this contribution, we present the latest results from IceCube as well as ongoing analyses using IceCube data, focusing on the works that look at the Galactic Halo, nearby galaxies, and galaxy clusters. Full article

(This article belongs to the Proceedings of The 23rd International Workshop on Neutrinos from Accelerators)

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11 pages, 2314 KiB

Open AccessProceeding Paper

Physics-Consistency Condition for Infinite Neural Networks and Experimental Characterization

by Sascha Ranftl and Shaoheng Guan

Phys. Sci. Forum 2023, 9(1), 15; https://doi.org/10.3390/psf2023009015 - 4 Dec 2023

Abstract

It has previously been shown that prior physics knowledge can be incorporated into the structure of an artificial neural network via neural activation functions based on (i) the correspondence under the infinite-width limit between neural networks and Gaussian processes if the central limit [...] Read more.

It has previously been shown that prior physics knowledge can be incorporated into the structure of an artificial neural network via neural activation functions based on (i) the correspondence under the infinite-width limit between neural networks and Gaussian processes if the central limit theorem holds and (ii) the construction of physics-consistent Gaussian process kernels, i.e., specialized covariance functions that ensure that the Gaussian process fulfills a priori some linear (differential) equation. Such regression models can be useful in many-query problems, e.g., inverse problems, uncertainty quantification or optimization, when a single forward solution or likelihood evaluation is costly. Based on a small set of training data, the learned model or “surrogate” can then be used as a fast approximator. The bottleneck is then for the surrogate to also learn efficiently and effectively from small data sets while at the same time ensuring physically consistent predictions. Based on this, we will further explore the properties of so-constructed neural networks. In particular, we will characterize (i) generalization behavior and (ii) the approximation quality or Gaussianity as a function of network width and discuss (iii) extensions from shallow to deep NNs. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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13 pages, 1585 KiB

Open AccessProceeding Paper

Bayesian Inference and Deep Learning for Inverse Problems

by Ali Mohammad-Djafari, Ning Chu, Li Wang and Liang Yu

Phys. Sci. Forum 2023, 9(1), 14; https://doi.org/10.3390/psf2023009014 - 1 Dec 2023

Abstract

Inverse problems arise anywhere we have an indirect measurement. In general, they are ill-posed to obtain satisfactory solutions, which needs prior knowledge. Classically, different regularization methods and Bayesian inference-based methods have been proposed. As these methods need a great number of forward and [...] Read more.

Inverse problems arise anywhere we have an indirect measurement. In general, they are ill-posed to obtain satisfactory solutions, which needs prior knowledge. Classically, different regularization methods and Bayesian inference-based methods have been proposed. As these methods need a great number of forward and backward computations, they become costly in computation, particularly when the forward or generative models are complex, and the evaluation of the likelihood becomes very costly. Using deep neural network surrogate models and approximate computation can become very helpful. However, in accounting for the uncertainties, we need first to understand Bayesian deep learning, and then we can see how we can use it for inverse problems. In this work, we focus on NN, DL, and, more specifically, the Bayesian DL particularly adapted for inverse problems. We first give details of Bayesian DL approximate computations with exponential families; then, we see how we can use them for inverse problems. We consider two cases: First, we consider the case where the forward operator is known and used as a physics constraint, and the second examines more general data-driven DL methods. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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10 pages, 574 KiB

Open AccessProceeding Paper

Proximal Nested Sampling with Data-Driven Priors for Physical Scientists

by Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price, Xiaohao Cai and Marcelo Pereyra

Phys. Sci. Forum 2023, 9(1), 13; https://doi.org/10.3390/psf2023009013 - 1 Dec 2023

Cited by 2

Abstract

Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for models with a log-convex likelihood, which are ubiquitous in the imaging sciences. The purpose of this article is two-fold. [...] Read more.

Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for models with a log-convex likelihood, which are ubiquitous in the imaging sciences. The purpose of this article is two-fold. First, we review proximal nested sampling in a pedagogical manner in an attempt to elucidate the framework for physical scientists. Second, we show how proximal nested sampling can be extended in an empirical Bayes setting to support data-driven priors, such as deep neural networks learned from training data. Full article

(This article belongs to the Proceedings of The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering)

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Physical Sciences Forum

Vol. 9

MaxEnt 2023

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NuFACT 2022

Vol. 7

ECU 2023

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ICPSDREE 2022

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ICEM 2022

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