Application of physics encoded neural networks to improve predictability of properties of complex multi-scale systemsdoi:10.1038/s41598-024-65304-wPredicting physical properties of complex multi-scale systems is a common challenge and demands analysis of various temporal and spatial scales. However, ...
Schematic of PINNs. Left: a standard fully-connected neural network parameterised by biases and weights\(\textbf{w}\)to approximate a function\(\textbf{u}(\textbf{x},\varvec{\mu })\). The set of model parameters to estimate is given by\(\varvec{\mu }\). Centre: automatic differentia...
The physics-based constraints are encoded as additional loss terms that penalize the model for violating physical laws, such as the PDE in (2). This hybrid approach allows PINNs to learn complex relationships between variables while also respecting physical constraints, with the ability to handle ...
We propose a new approach to the solution of the wave propagation and full waveform inversions (FWIs) based on a recent advance in deep learning called physics-informed neural networks (PINNs). In this study, we present an algorithm for PINNs applied to the acoustic wave equation and test th...
target DEs together with initial and boundary conditions) are encoded as NN training framework. The physics-informed NN (PINN) has been successfully applied to solve many DEs, including heat equation [9], Burger equation [10], Navier Stoke’s equation [11], Schrödinger equation [12], Hamilt...
Furthermore, a priori knowledge of the subsurface structure can be seamlessly encoded in PINNs formulation. We find that the current state-of-the-art PINNs provide good results for the forward model, even though spectral element or finite difference methods are more efficient and accurate. More ...
residuals, while the I/BCs are hard-encoded in the network to ensure forcible satisfaction (e.g., periodic boundary padding). The networks are further enhanced by autoregressive and residual connections that explicitly simulate time marching. The performance of our proposed methods has been assessed...
This paper presents a physics-informed neural network (PINN) approach for monitoring the health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown parameters in a “mean value” model, and anticipate maintenance requirements.
In recent years, physics-informed neural networks (PINN) have been used to solve stiff-PDEs mostly in the 1D and 2D spatial domain. PINNs still experience
Collocation points in PINN-CHK act as anchor points where the neural network learns to approx- imate the solution while respecting the cement hydration physics encoded in the governing equations. To facilitate the effective training of the model, the preprocessing step of this work involves min–...