2 citations · 3 across the 5 of their papers we have counts for
6 papers
Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
Anas Jnini, Elham Kiyani, Khemraj Shukla +5
Efficient and robust optimization is essential for neural networks, enabling scientific machine learning models to converge rapidly to very high accuracy -- faithfully capturing co…
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-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…
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…
cuVegas: Accelerate Multidimensional Monte Carlo Integration through a Parallelized CUDA-based Implementation of the VEGAS Enhanced Algorithm
Emiliano Tolotti, Anas Jnini, Flavio Vella +1
This paper introduces cuVegas, a CUDA-based implementation of the Vegas Enhanced Algorithm (VEGAS+), optimized for multi-dimensional integration in GPU environments. The VEGAS+ alg…
Gauss-Newton Natural Gradient Descent for Physics-Informed Computational Fluid Dynamics
Anas Jnini, Flavio Vella, Marius Zeinhofer
We propose Gauss-Newton's method in function space for the solution of the Navier-Stokes equations in the physics-informed neural network (PINN) framework. Upon discretization, thi…