39 citations · 39 across the 4 of their papers we have counts for
5 papers
Variational Augmented Invertible Koopman Autoencoder for probabilistic time series forecasting
Anthony Frion, Lucas Drumetz, Guillaume Tochon +3
Neural Koopman autoencoder models have been shown to successfully build a latent embedding with linear dynamics for arbitrary dynamical systems, enabling strong performance in long…
ADDA: a Modular Framework for Representing, Simulating and Assimilating Dynamics with End-to-end Differentiability
Anthony Frion, Vien Minh Nguyen-Thanh, Ali Can Bekar +3
Data assimilation (DA) is an essential tool for prediction and understanding in the geosciences. DA combines simulation programs representing scientific knowledge with observations…
Physics-based machine learning for mantle convection simulations
Siddhant Agarwal, Ali Can Bekar, Christian Hüttig +2
Mantle convection simulations are an essential tool for understanding how rocky planets evolve. However, the poorly known input parameters to these simulations, the non-linear depe…
Multiphysics discovery with moving boundaries using Ensemble SINDy and Peridynamic Differential Operator
A. C. Bekar, E. Haghighat, E. Madenci
This study proposes a novel framework for learning the underlying physics of phenomena with moving boundaries. The proposed approach combines Ensemble SINDy and Peridynamic Differe…
An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator
A. Mavi, A. C. Bekar, E. Haghighat +1
This study presents a novel unsupervised convolutional Neural Network (NN) architecture with nonlocal interactions for solving Partial Differential Equations (PDEs). The nonlocal P…