Physics-Informed Deep Learning of Rate-and-State Fault Friction
- URL: http://arxiv.org/abs/2312.09403v1
- Date: Thu, 14 Dec 2023 23:53:25 GMT
- Title: Physics-Informed Deep Learning of Rate-and-State Fault Friction
- Authors: Cody Rucker and Brittany A. Erickson
- Abstract summary: We develop a multi-network PINN for both the forward problem and for direct inversion of nonlinear fault friction parameters.
We present the computational PINN framework for strike-slip faults in 1D and 2D subject to rate-and-state friction.
We find that the network for the parameter inversion at the fault performs much better than the network for material displacements to which it is coupled.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Direct observations of earthquake nucleation and propagation are few and yet
the next decade will likely see an unprecedented increase in indirect, surface
observations that must be integrated into modeling efforts. Machine learning
(ML) excels in the presence of large data and is an actively growing field in
seismology. However, not all ML methods incorporate rigorous physics, and
purely data-driven models can predict physically unrealistic outcomes due to
observational bias or extrapolation. Our work focuses on the recently emergent
Physics-Informed Neural Network (PINN), which seamlessly integrates data while
ensuring that model outcomes satisfy rigorous physical constraints. In this
work we develop a multi-network PINN for both the forward problem as well as
for direct inversion of nonlinear fault friction parameters, constrained by the
physics of motion in the solid Earth, which have direct implications for
assessing seismic hazard. We present the computational PINN framework for
strike-slip faults in 1D and 2D subject to rate-and-state friction. Initial and
boundary conditions define the data on which the PINN is trained. While the
PINN is capable of approximating the solution to the governing equations to
low-errors, our primary interest lies in the network's capacity to infer
friction parameters during the training loop. We find that the network for the
parameter inversion at the fault performs much better than the network for
material displacements to which it is coupled. Additional training iterations
and model tuning resolves this discrepancy, enabling a robust surrogate model
for solving both forward and inverse problems relevant to seismic faulting.
Related papers
- Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models.
Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML)
We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
arXiv Detail & Related papers (2024-07-18T15:44:23Z) - Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures [6.483318568088176]
PiML method integrates scientific principles and physical laws into deep neural networks to model seismic responses of nonlinear structures.
Manipulating the equation of motion helps learn system nonlinearities and confines solutions within physically interpretable results.
Result handles complex data better than existing physics-guided LSTM models and outperforms other non-physics data-driven networks.
arXiv Detail & Related papers (2024-02-28T02:16:03Z) - Physics-Informed Neural Networks with Hard Linear Equality Constraints [9.101849365688905]
This work proposes a novel physics-informed neural network, KKT-hPINN, which rigorously guarantees hard linear equality constraints.
Experiments on Aspen models of a stirred-tank reactor unit, an extractive distillation subsystem, and a chemical plant demonstrate that this model can further enhance the prediction accuracy.
arXiv Detail & Related papers (2024-02-11T17:40:26Z) - DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via
Physics Simulation [81.11585774044848]
We present DeepSimHO, a novel deep-learning pipeline that combines forward physics simulation and backward gradient approximation with a neural network.
Our method noticeably improves the stability of the estimation and achieves superior efficiency over test-time optimization.
arXiv Detail & Related papers (2023-10-11T05:34:36Z) - SimPINNs: Simulation-Driven Physics-Informed Neural Networks for
Enhanced Performance in Nonlinear Inverse Problems [0.0]
This paper introduces a novel approach to solve inverse problems by leveraging deep learning techniques.
The objective is to infer unknown parameters that govern a physical system based on observed data.
arXiv Detail & Related papers (2023-09-27T06:34:55Z) - Physics-Informed Neural Networks for Material Model Calibration from
Full-Field Displacement Data [0.0]
We propose PINNs for the calibration of models from full-field displacement and global force data in a realistic regime.
We demonstrate that the enhanced PINNs are capable of identifying material parameters from both experimental one-dimensional data and synthetic full-field displacement data.
arXiv Detail & Related papers (2022-12-15T11:01:32Z) - Learning Physical Dynamics with Subequivariant Graph Neural Networks [99.41677381754678]
Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics.
Physical laws abide by symmetry, which is a vital inductive bias accounting for model generalization.
Our model achieves on average over 3% enhancement in contact prediction accuracy across 8 scenarios on Physion and 2X lower rollout MSE on RigidFall.
arXiv Detail & Related papers (2022-10-13T10:00:30Z) - Robust Learning of Physics Informed Neural Networks [2.86989372262348]
Physics-informed Neural Networks (PINNs) have been shown to be effective in solving partial differential equations.
This paper shows that a PINN can be sensitive to errors in training data and overfit itself in dynamically propagating these errors over the domain of the solution of the PDE.
arXiv Detail & Related papers (2021-10-26T00:10:57Z) - Characterizing possible failure modes in physics-informed neural
networks [55.83255669840384]
Recent work in scientific machine learning has developed so-called physics-informed neural network (PINN) models.
We demonstrate that, while existing PINN methodologies can learn good models for relatively trivial problems, they can easily fail to learn relevant physical phenomena even for simple PDEs.
We show that these possible failure modes are not due to the lack of expressivity in the NN architecture, but that the PINN's setup makes the loss landscape very hard to optimize.
arXiv Detail & Related papers (2021-09-02T16:06:45Z) - Simultaneous boundary shape estimation and velocity field de-noising in
Magnetic Resonance Velocimetry using Physics-informed Neural Networks [70.7321040534471]
Magnetic resonance velocimetry (MRV) is a non-invasive technique widely used in medicine and engineering to measure the velocity field of a fluid.
Previous studies have required the shape of the boundary (for example, a blood vessel) to be known a priori.
We present a physics-informed neural network that instead uses the noisy MRV data alone to infer the most likely boundary shape and de-noised velocity field.
arXiv Detail & Related papers (2021-07-16T12:56:09Z) - Non-Singular Adversarial Robustness of Neural Networks [58.731070632586594]
Adrial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations.
We formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
arXiv Detail & Related papers (2021-02-23T20:59:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.