Search Results for author: Mario Lino

Found 5 papers, 1 papers with code

Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks

no code implementations5 May 2022 Mario Lino, Stathi Fotiadis, Anil A. Bharath, Chris Cantwell

Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice.

Graph Neural Network

REMuS-GNN: A Rotation-Equivariant Model for Simulating Continuum Dynamics

no code implementations5 May 2022 Mario Lino, Stati Fotiadis, Anil A. Bharath, Chris Cantwell

Numerical simulation is an essential tool in many areas of science and engineering, but its performance often limits application in practice or when used to explore large parameter spaces.

Inductive Bias

Disentangled Generative Models for Robust Prediction of System Dynamics

1 code implementation26 Aug 2021 Stathi Fotiadis, Mario Lino, Shunlong Hu, Stef Garasto, Chris D Cantwell, Anil Anthony Bharath

Deep neural networks have become increasingly of interest in dynamical system prediction, but out-of-distribution generalization and long-term stability still remains challenging.

Disentanglement Out-of-Distribution Generalization

Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks

no code implementations9 Jun 2021 Mario Lino, Chris Cantwell, Anil A. Bharath, Stathi Fotiadis

Continuum mechanics simulators, numerically solving one or more partial differential equations, are essential tools in many areas of science and engineering, but their performance often limits application in practice.

Graph Neural Network Inductive Bias

Simulating Surface Wave Dynamics with Convolutional Networks

no code implementations1 Dec 2020 Mario Lino, Chris Cantwell, Stathi Fotiadis, Eduardo Pignatelli, Anil Bharath

We investigate the performance of fully convolutional networks to simulate the motion and interaction of surface waves in open and closed complex geometries.

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