Scientific Machine Learning and Uncertainty Quantification
A special issue of Mathematical and Computational Applications (ISSN 2297-8747).
Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 1136
Special Issue Editors
Interests: numerical analysis; uncertainty quantification and statistical learning; fractional LES turbulence modeling; anomalous transport; multiscale material failure modeling
2. Institute for Computational & Mathematical Engineering, Stanford University, Stanford, CA 94305, USA
Interests: nonlocal and fractional problems; machine learning; optimization; uncertainty quantification; data assimilation
Special Issue Information
Dear Colleagues,
Uncertainty quantification and scientific machine learning can be essentially motivated by a range of vital applications, such as life-threatening events (e.g., pandemics, disease propagation, global warming, wildfires, hurricanes, in addition to limited water and food resources) and medical applications (e.g., cancer growth, digital surgery, precision medicine, informed medical decision making, tissue synthesis/engineering), and, more in general, in engineering applications such as plasma physics, subsurface transport, turbulence, additive manufacturing for complex multiscale materials, aging electrical systems and power grids, failure processes in mechanical structures, and more.
The main challenges in such applications include (but are not limited to): ill-posedness, necessary-to-sufficient training data/cost, lack of rigorous mathematical theories for learning paradigms, lack of a priori estimates for predictability, curse of dimensionality, noisy/gappy/sparse data, large and multimodal/physics/scale data, model form learning, overfitting/underfitting, lack of fidelity and generalization of surrogate models, long-time integration/learning, reliable data assimilation, and model calibration away from the proximity of observables.
This Special Issue welcomes the submission of creative manuscripts that address the aforementioned challenges either theoretically or computationally in a novel fashion in the context of stochastic integer-to-fractional differential equations in addition to uncertain local-to-nonlocal mathematical models with the ultimate purpose of developing the new generation of AI-enabled science and engineering.
Dr. Mohsen Zayernouri
Dr. Marta D'Elia
Guest Editors
Manuscript Submission Information
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