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08/09/2023
Learning of discrete models of variational PDEs from data
We show how to learn discrete field theories from observational data of ...
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02/16/2023
Learning discrete Lagrangians for variationalPDEs from data and detection of travelling waves
The article shows how to learn models of dynamical systems from data whi...
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01/19/2023
Hamiltonian Neural Networks with Automatic Symmetry Detection
Recently, Hamiltonian neural networks (HNN) have been introduced to inco...
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11/20/2022
Approximate Uncertainty Propagation for Continuous Gaussian Process Dynamical Systems
When learning continuous dynamical systems with Gaussian Processes, comp...
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11/20/2022
Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery
By one of the most fundamental principles in physics, a dynamical system...
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03/23/2022
Verification of safety critical control policies using kernel methods
Hamilton-Jacobi reachability methods for safety-critical control have be...
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12/23/2021
Variational integration of learned dynamical systems
The principle of least action is one of the most fundamental physical pr...
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08/05/2021
Symplectic integration of learned Hamiltonian systems
Hamiltonian systems are differential equations which describe systems in...
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04/25/2021
Superconvergence of Galerkin variational integrators
We study the order of convergence of Galerkin variational integrators fo...
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04/08/2021
Efficient time stepping for numerical integration using reinforcement learning
Many problems in science and engineering require the efficient numerical...
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11/10/2020
