A hybrid physics-informed framework using polynomials and neural networks approximates invariant manifolds of discrete-time dynamical systems with nonlinear exosystems and shows higher accuracy than pure polynomial or neural approaches on bioreactor and car-following benchmarks.
Understanding and mitigating gradient flow pathologies in physics-informed neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2025 2verdicts
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A neural operator is trained once including a PDE residual penalty and then reused inside gradient-based optimization to solve multiple PDE-constrained tracking control problems.
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Invariant Manifolds of Discrete-time Dynamical Systems with Nonlinear Exosystems via Hybrid Physics-Informed Neural Networks
A hybrid physics-informed framework using polynomials and neural networks approximates invariant manifolds of discrete-time dynamical systems with nonlinear exosystems and shows higher accuracy than pure polynomial or neural approaches on bioreactor and car-following benchmarks.
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Employing Deep Neural Operators for PDE control by decoupling training and optimization
A neural operator is trained once including a PDE residual penalty and then reused inside gradient-based optimization to solve multiple PDE-constrained tracking control problems.