Learning Dynamics of Spring-Mass Models with Physics-Informed Graph Neural Networks

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Related publications (31)

Role of stochastic noise and generalization error in the time propagation of neural-network quantum states

Neural-network quantum states (NQS) have been shown to be a suitable variational ansatz to simulate out-of-equilibrium dynamics in two-dimensional systems using timedependent variational Monte Carlo (t-VMC). In particular, stable and accurate time propagat ...

SCIPOST FOUNDATION2022

Deep learning-based galaxy image deconvolution

Frédéric Courbin, Pascale Jablonka, Jean-Luc Starck, Utsav Akhaury, Kevin Michalewicz

With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method woul ...

FRONTIERS MEDIA SA2022

Numerical Studies of Brillouin-induced Polarization Pulling of Probe and Pump in Weakly-Birefringent Optical fibers with Applications to BOTDA

Luc Thévenaz, Marcelo Alfonso Soto Hernandez

Numerical solution of the involved governing equations confirm that the commonly used two orthogonal sets of gain- and loss-probes in BOTDA, differently affect the evolution of the pump state-of- polarization, thereby potentially compromising the minimizat ...

Optica Publishing Group2022

Exploiting Marginal Stability in Slow-Fast Quasilinear Dynamical Systems

Alessia Ferraro

The accurate investigation of many geophysical phenomena via direct numerical simulations is computationally not possible nowadays due to the huge range of spatial and temporal scales to be resolved. Therefore advances in this field rely on the development ...

EPFL2022

Subharmonic parametric instability in nearly brimful circular cylinders: a weakly nonlinear analysis

François Gallaire, Francesco Viola, Alessandro Bongarzone

In labscale Faraday experiments, meniscus waves respond harmonically to small-amplitude forcing without threshold, hence potentially cloaking the instability onset of parametric waves. Their suppression can be achieved by imposing a contact line pinned at ...

Cambridge University Press2022

Reduced basis methods for numerical room acoustic simulations with parametrized boundaries

Jan Sickmann Hesthaven, Hermes Sampedro Llopis

The use of model-based numerical simulations of wave propagation in rooms for engineering applications requires that acoustic conditions for multiple parameters are evaluated iteratively, which is computationally expensive. We present a reduced basis metho ...

ACOUSTICAL SOC AMER AMER INST PHYSICS2022

Recursive Input And State Estimation: A General Framework For Learning From Time Series With Missing Data

Robert West, Alberto Garcia Duran

Time series with missing data are signals encountered in important settings for machine learning. Some of the most successful prior approaches for modeling such time series are based on recurrent neural networks that transform the input and previous state ...

IEEE2021

Physics-enhanced machine learning with symmetry-adapted and long-range representations

Andrea Grisafi

Theoretical and computational approaches to the study of materials and molecules have, over the last few decades, progressed at an exponential rate. Yet, the possibility of producing numerical predictions that are on par with experimental measurements is t ...

EPFL2021

Work fluctuations in the active Ornstein-Uhlenbeck particle model

Francesco Cagnetta

We study the large deviations of the power injected by the active force for an active Ornstein-Uhlenbeck particle (AOUP), free or in a confining potential. For the free-particle case, we compute the rate function analytically in d-dimensions from a saddle- ...

IOP Publishing Ltd2021

Mathematical and numerical models for the cardiac electromechanical function

Alfio Quarteroni, Francesco Regazzoni

This paper deals with the mathematical model that describes the function of the human heart. More specifically, it addresses the equations that express the electromechanical process, that is the mechanical deformation (contraction and relaxation) of the he ...

EUROPEAN MATHEMATICAL SOC-EMS2021

Robust Differentiable SVD

Pascal Fua, Mathieu Salzmann, Zheng Dang, Yinlin Hu, Wei Wang

Eigendecomposition of symmetric matrices is at the heart of many computer vision algorithms. However, the derivatives of the eigenvectors tend to be numerically unstable, whether using the SVD to compute them analytically or using the Power Iteration (PI) ...

2021

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