New Trends in Computational Methods and Stochastic Modelling in Scientific Machine Learning

Dear Colleagues,

The editorial team of Mathematics is pleased to announce a call for manuscripts for an upcoming Special Issue dedicated to the theme of "New Trends in Computational Methods and Stochastic Modelling in Scientific Machine Learning". This Special Issue is dedicated to highlighting the most recent progress and computational methodologies in the scientific computing realm with a focus on stochastic dynamical systems and machine learning techniques, with applications spanning from mechanics, physics, and biology to engineering and computer science. We aim to provide a forum for researchers in the fields of applied mathematics, computational science, physical science, and engineering to share their groundbreaking research with an international audience.

# Scope of the Special Issue: 

We invite original research articles, review papers, and short communications that explore the integration of computational techniques and stochastic modelling with scientific machine learning in a general sense. Suitable topics include but are not limited to the following: 

+ New computational algorithms for solving stochastic or high-dimensional differential equations;

+ Stochastic particle methods for probability flow;

+ New algorithms of flow-based methods for generative models in AI;

+ Simulation of challenging problems in randomly perturbed dynamical systems; 

+ Rare transition events in molecular systems;

+ Data-driven techniques to understand complex dynamical systems; 

+ Data mining and model reduction of big data in high-dimensional and complex dynamical systems;

+ Algorithms based on the Koopman operator or variational formulation for dynamical systems;

+ Applications of machine learning and artificial intelligence to complex dynamical systems.

We welcome your contributions to this Special Issue and look forward to showcasing innovative work that continues to propel the field forward.

Prof. Xiang Zhou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• stochastic continuous models
• Monte Carlo method
• scientific machine learning
• deep learning