2 citations · 3 across the 7 of their papers we have counts for
7 papers
Toward GPU-Resident Climate Models: A Feasibility Study on Lossy Compression for the Spherical Harmonic Transform's Communication Bottleneck
Lorenzo Breschi, Flavio Vella
Operational pseudospectral atmospheric models such as the ECMWF Integrated Forecasting System (IFS) run today almost exclusively on CPUs; GPU ports are under active development but…
Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization
Francesco Pio Monaco, Elia Cunegatti, Flavio Vella +1
Post-training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance. While several compression approaches…
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…