Scientific Machine Learning, emerging topics

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A comparative review of regression techniques for predicting the physical characteristics of ship engines

A Reduced Order Approach for Artificial Neural Networks applied to Object Recognition

Data-driven Inference of Chemical Reaction Networks via Graph-based Variational Autoencoders

Deciphering Thrombosis: Advancing Multi-Scale Modeling with THRONE

Deep learning approaches for meshless surrogate models in variable geometries

Deep unfolding for matrix factorizations

DeepONet-based inverse approximator in matrix-free applications

Delta-PINNs: physics-informed neural networks on complex geometries

Discovery of Dirichlet-to-Neumann Maps on Graphs via Gaussian Processes

Domain Decomposition and Machine Learning - Two Mutually Beneficial Areas

Graph-based machine learning approaches for model order reduction

Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes

Learning stability on graphs

Mesh-informed reduced order models for aneurysm risk prediction

Opening

Operator Learning for Ionic Models in Biomathematics

Panel Session

Parameterization learning for scattered data adaptive spline fitting

PINA: a PyTorch Framework for Solving Differential Equations with Deep Learning

Poster Session

Pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs in small data regimes

Rate estimates of ONets

Registration

SOGA: Second Order Gaussian Approximation of Probabilistic Programs

Solving the inverse problem in complex systems via machine learning: challenges and perspectives

Turbulence Subgrid Closure for the Lattice Boltzmann Method via Artificial Neural Networks

Unfolding the Kalman Filter iterations in a machine learning framework

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6/18/24

6/19/24

6/20/24

6/21/24

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