A learned continuous Lagrangian paired with Euler-Lagrange residual minimization on local patches enables stable long-range forecasting of PDE-governed systems while generalizing across boundary conditions.
Physics-based deep learning
4 Pith papers cite this work. Polarity classification is still indexing.
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SWIM is a single-instance imitation method for learning and generalizing physically simulated swimming motions to new environments, bodies, and styles.
GEN is a neural network that solves PDEs by constructing explicit function approximations from basis functions based on prior PDE knowledge, yielding more robust and extensible solutions than standard PINNs.
This perspective paper categorizes hybrid architectures for combining mechanistic and data-driven models using residual learning, Neural ODEs, and solver-in-the-loop to model neurological disorder progression.
citing papers explorer
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Learned Lagrangian Models of PDEs via Euler-Lagrange Residual Minimization
A learned continuous Lagrangian paired with Euler-Lagrange residual minimization on local patches enables stable long-range forecasting of PDE-governed systems while generalizing across boundary conditions.
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SWIM: Single-Instance Whole-Body Imitation for swiMming
SWIM is a single-instance imitation method for learning and generalizing physically simulated swimming motions to new environments, bodies, and styles.
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General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
GEN is a neural network that solves PDEs by constructing explicit function approximations from basis functions based on prior PDE knowledge, yielding more robust and extensible solutions than standard PINNs.
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Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming
This perspective paper categorizes hybrid architectures for combining mechanistic and data-driven models using residual learning, Neural ODEs, and solver-in-the-loop to model neurological disorder progression.