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Probabilistic Modeling and Scientific Machine Learning for Computational Physics and Engineering

Year: 2025

Type: dissertation

Abstract: This PhD thesis integrates probabilistic machine learning with physics-based modeling to improve computational methods in science and engineering. It presents strategies to address challenges such as limited data, noise, and differentiability. Key contributions include a probabilistic data-driven turbulence model, a framework for sustainable concrete design combining model discovery and optimization, and a novel algorithm for optimizing high-dimensional, physics-based simulations.

Source: mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich)

DOI: 10.14459/2025md1752562

Author: Agrawal, Atul

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6942094369

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