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03/23/2023
Differentiable hybrid neural modeling for fluid-structure interaction
Solving complex fluid-structure interaction (FSI) problems, which are de...
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11/26/2022
PatchGT: Transformer over Non-trainable Clusters for Learning Graph Representations
Recently the Transformer structure has shown good performances in graph ...
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10/14/2022
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
Nonlinear dynamics are ubiquitous in science and engineering application...
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05/09/2022
Predicting parametric spatiotemporal dynamics by multi-resolution PDE structure-preserved deep learning
Although recent advances in deep learning (DL) have shown a great promis...
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01/22/2022
Predicting Physics in Mesh-reduced Space with Temporal Attention
Graph-based next-step prediction models have recently been very successf...
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07/31/2021
Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control
Model-based reinforcement learning (MBRL) is believed to have much highe...
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07/16/2021
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
Despite the great promise of the physics-informed neural networks (PINNs...
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03/23/2020
A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems
Mathematical modeling and simulation of complex physical systems based o...
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11/10/2019
Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning
Projection-based model reduction has become a popular approach to reduce...
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12/15/2018
Adding Constraints to Bayesian Inverse Problems
Using observation data to estimate unknown parameters in computational m...
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08/19/2018
