3 papers
cs.LG2025
Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks
Anas Jnini, Flavio Vella
Natural-gradient methods markedly accelerate the training of Physics-Informed Neural Networks (PINNs), yet their Gauss--Newton update must be solved in the parameter space, incurri…
physics.flu-dyn2025
Physics-constrained DeepONet for Surrogate CFD models: a curved backward-facing step case
Anas Jnini, Harshinee Goordoyal, Sujal Dave +3
The Physics-Constrained DeepONet (PC-DeepONet), an architecture that incorporates fundamental physics knowledge into the data-driven DeepONet model, is presented in this study. Thi…
cs.LG2025
Riemann Tensor Neural Networks: Learning Conservative Systems with Physics-Constrained Networks
Anas Jnini, Lorenzo Breschi, Flavio Vella
Divergence-free symmetric tensors (DFSTs) are fundamental in continuum mechanics, encoding conservation laws such as mass and momentum conservation. We introduce Riemann Tensor Neu…