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05/26/2023
A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems
This work presents a multi-resolution physics-informed recurrent neural ...
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01/26/2023
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling
One of the most challenging and consequential problems in climate modeli...
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09/03/2022
Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
Physics-constrained data-driven computing is an emerging computational p...
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08/18/2022
Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions
In this paper, we develop a physics-informed neural network (PINN) model...
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03/03/2022
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery
Numerical modeling and simulation have become indispensable tools for ad...
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08/29/2021
Flying Through a Narrow Gap Using End-to-end Deep Reinforcement Learning Augmented with Curriculum Learning and Sim2Real
Traversing through a tilted narrow gap is previously an intractable task...
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06/21/2021
Physics-constrained deep neural network method for estimating parameters in a redox flow battery
In this paper, we present a physics-constrained deep neural network (PCD...
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01/29/2020
Patient Specific Biomechanics Are Clinically Significant In Accurate Computer Aided Surgical Image Guidance
Augmented Reality is used in Image Guided surgery (AR IG) to fuse surgic...
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12/06/2019
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
Data assimilation for parameter and state estimation in subsurface trans...
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07/26/2019