Seminar by Professor Jianxun Wang, Assistant Professor, Aerospace & Mechanical Engineering, University of Notre Dame
Date: Thursday, March 21
Time: 2:00 p.m.–3:00 p.m.
Location: E339 Westgate Building
Neural Differentiable Physics: Unifying Numerical PDEs and Deep Learning for Data-Augmented Computational Physics
Speaker Bio:
Dr. Wang has a multidisciplinary research background, cutting cross scientific machine learning, data assimilation, Bayesian inference, uncertainty quantification, and computational mechanics. In particular, his research focus is at the interface of scientific machine learning, data-driven modeling, scientific computing, UQ, and computational mechanics. He has (co-) led research projects sponsored by multiple agencies, including NSF, ONR, AFSOR, DARPA, CICP, etc. Dr. Wang received the 2021 NSF CAREER Award, 2023 ONR YIP Award, and the 2022 Top 10 Outstanding Chinese American Youth Award. Dr. Wang also serves on the editorial board of Nature Scientific Report and Frontier in Physiology, and he is a member-at-large of the US. Association of Computational Mechanics (USACM) Technical Thrust Area on Uncertainty Quantification and Technical Thrust Area on Data-Driven Modeling.
Hosted by:
Romit Maulik,
Assistant Professor and ICDS Co-Hire, IST, Pennsylvania State University
For any questions, please reach out to Dr. Maulik.
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Seminar Abstract:
Predictive modeling and simulation are essential for understanding, predicting, and controlling complex physical processes across many engineering disciplines. However, traditional numerical models, which are based on first principles, face significant challenges, especially for complex systems involving multiple interacting physics across a wide range of spatial and temporal scales. (1) A primary obstacle stems from our often-incomplete understanding of the underlying physics, which results in inadequate mathematical models that fail to accurately capture system behavior. (2) Additionally, the high computational demands of traditional solvers represent another substantial barrier, especially when real-time control or many repeated model queries are required, as in design optimization, inference, and uncertainty quantification. Fortunately, the continual evolution of sensing technology and the exponential increase in data availability have opened new avenues for the development of data-driven computational modeling frameworks. Bolstered by advanced machine learning and GPU computing techniques, these models hold the promise of greatly enhancing our predictive capabilities, effectively tackling the challenges posed by traditional numerical models. While data science and machine learning offer novel methods for computational mechanics models, challenges persist, such as the need for extensive data, limited generalizability, and lack of interpretability. Addressing existing challenges for predictive modeling issues requires innovative computational methods that integrate advanced machine learning techniques with physics principles. This talk will introduce some of our efforts along this direction, spotlighting the Neural Differentiable Physics, a novel SciML framework unifying classic numerical PDEs and advanced deep learning models for computational modeling of complex physical systems. Our approach centers on the integration of numerical PDE operators into neural architectures, enabling the fusion of prior knowledge of known physics, multi-resolution data, numerical techniques, and deep neural networks through differentiable programming. The way for integrating physics into the deep learning model represents a novel departure from existing SciML frameworks, such as Physics-Informed Neural Networks (PINNs). By combining the strengths of known physical principles and established numerical techniques with cutting-edge deep learning and AI technology, this innovative framework promises to inaugurate a new era in the understanding and modeling of complex physical systems, with far-reaching implications for science and engineering applications.