Publication

PINNs applied to dynamic friction

2025

Abstract

Dynamic friction plays a key role in engineering and science, especially in earth-quake mechanics, where it impacts fault dynamics and energy distribution. Our goal is to better understand this complex process to improve predictions and refine earthquake mechanics based on existing developments to study the phenomenon of stick-slip. For this project, we use Physics-Informed Neural Networks (PINNs), a modern method that combines physical laws with neural networks to solve non-linear equations. The work is based on the study ”Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Non-linear Partial Differential Equations” by M. Raissi et al.

Official source

https://infoscience.epfl.ch/entities/publication/4c6d9a1b-f221-44fd-8f10-72732f48793a

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2025

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/4c6d9a1b-f221-44fd-8f10-72732f48793a

About this result

Related concepts (32)

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Related publications (32)

PINNs applied to dynamic friction

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