- MERL Seminar Series.)
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Date & Time:
Wednesday, March 1, 2023; 1:00 PM
High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such large-scale systems often rely on dimensionality reduction to make solutions computationally tractable in real-time. Common existing paradigms for dimensionality reduction include linear methods, such as the singular value decomposition (SVD), and nonlinear methods, such as variants of convolutional autoencoders (CAE). However, these encoding techniques lack the ability to efficiently represent the complexity associated with spatio-temporal data, which often requires variable geometry, non-uniform grid resolution, adaptive meshing, and/or parametric dependencies. To resolve these practical engineering challenges, we propose a general framework called Neural Implicit Flow (NIF) that enables a mesh-agnostic, low-rank representation of large-scale, parametric, spatial-temporal data. NIF consists of two modified multilayer perceptrons (MLPs): (i) ShapeNet, which isolates and represents the spatial complexity, and (ii) ParameterNet, which accounts for any other input complexity, including parametric dependencies, time, and sensor measurements. We demonstrate the utility of NIF for parametric surrogate modeling, enabling the interpretable representation and compression of complex spatio-temporal dynamics, efficient many-spatial-query tasks, and improved generalization performance for sparse reconstruction.
Rensselaer Polytechnic Institute
Shaowu Pan is currently a tenure-track assistant professor in the Department of Mechanical, Aerospace, and Nuclear Engineering at RPI. He is also affiliated with the Rensselaer-IBM Artificial Intelligence Research Collaboration (AIRC). He received M.S. and Ph.D. in Aerospace Engineering and Scientific Computing from the University of Michigan, Ann Arbor in April 2021. Then he started as a Postdoctoral Scholar in the AI Institute in Dynamic Systems at the University of Washington, Seattle from 2021 to 2022. His research interests lie in the intersection between computational fluid dynamics, data-driven modeling of complex systems, scientific machine learning, and dynamical systems. He has published his work in journals like the Journal of Fluid Mechanics, AIAA Journal, SIAM Applied Dynamical Systems, Chaos, Computer Methods in Applied Mechanics and Engineering, Computational Mechanics, etc.