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We address two major challenges in scientific machine learning (SciML): interpretability and computational efficiency.
Viscosity solutions of Hamilton-Jacobi equations
Michael G Crandall and Pierre-Louis Lions · 1983
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Differential games and representation formulas for solutions of Hamilton-Jacobi-Isaacs equations
Lawrence C. Evans and Panagiotis E. Souganidis · 1984
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The taxation principle and multi-time Hamilton-Jacobi equations
J-C. Rochet · 1985
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Hopf Formula and Multitime Hamilton-Jacobi Equations
P. L. Lions and J-C. Rochet · 1986
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Optimal control and viscosity solutions of Hamilton-Jacobi-Bellman equations
Martino Bardi and Italo Capuzzo-Dolcetta · 1997
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Applied linear regression , volume 528
Sanford Weisberg · 2005
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Max-plus methods for nonlinear control and estimation
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Optimal control: linear quadratic methods
Brian DO Anderson and John B Moore · 2007
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Overcoming catastrophic forgetting in neural networks
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DGM: A deep learning algorithm for solving partial differential equations
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Three scenarios for continual learning
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Machine learning interpretability: A survey on methods and metrics
Diogo V Carvalho, Eduardo M Pereira, and Jaime S Cardoso · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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On decomposition models in imaging sciences and multi-time Hamilton–Jacobi partial differential equations
Jérôme Darbon and Tingwei Meng · 2020
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Underactuated Robotics
Russ Tedrake · 2023
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Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems
Paula Chen, Tingwei Meng, Zongren Zou, Jérôme Darbon, and George Em Karniadakis · 2024
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Zongren Zou, Xuhui Meng, and George Em Karniadakis · 2024
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