The limits of exactness: On the failure of automatic differentiation in physics-informed machine learning
cs.LG
Submitted: 2026-09-27
Updated: 2026-09-27
Terminology
Sources
- GenVoid: Uncertainty-Aware Learning of Subsurface Material Defects with an Experimentally Validated Physics-Informed Generative Model
- Lagrangian Neural Networks
- Symplectic Recurrent Neural Networks
- Solution multiplicity and effects of data and eddy viscosity on Navier-Stokes solutions inferred by physics-informed neural networks
- Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients
- An invariance constrained deep learning network for PDE discovery
- Improving physics-informed DeepONets with hard constraints
- Deep Neural Networks with Symplectic Preservation Properties
- Learning Hamiltonian Systems with Pseudo-symplectic Neural Network
- Symplectic Neural Operators for Learning Infinite Dimensional Hamiltonian Systems
- Neural non-canonical Hamiltonian dynamics for long-time simulations
- WANCO: Weak Adversarial Networks for Constrained Optimization problems
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