[HTML][HTML] Inverse Physics-Informed Neural Networks for transport models in porous materials
Abstract Physics-Informed Neural Networks (PINN) are a machine learning tool that can be
used to solve direct and inverse problems related to models described by Partial Differential …
used to solve direct and inverse problems related to models described by Partial Differential …
Time series predictions in unmonitored sites: A survey of machine learning techniques in water resources
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing
challenge for water resources science. The majority of the world's freshwater resources have …
challenge for water resources science. The majority of the world's freshwater resources have …
A physics-informed deep learning approach for solving strongly degenerate parabolic problems
Abstract In recent years, Scientific Machine Learning (SciML) methods for solving Partial
Differential Equations (PDEs) have gained increasing popularity. Within such a paradigm …
Differential Equations (PDEs) have gained increasing popularity. Within such a paradigm …
Physics-informed identification of PDEs with LASSO regression, examples of groundwater-related equations
In recent years, the application of machine learning methods in the derivation of physical
governing equations has gained significant attention. This has become increasingly relevant …
governing equations has gained significant attention. This has become increasingly relevant …
Physics-informed neural networks in groundwater flow modeling: Advantages and future directions
In recent years, there has been enormous development in soft computing, especially
artificial intelligence (AI), which has developed robust methods for solving complex …
artificial intelligence (AI), which has developed robust methods for solving complex …
PGNM: Using Physics-Informed Gated Recurrent Units Network Method to capture the dynamic data feature propagation process of PDEs
C Chen - Chaos, Solitons & Fractals, 2024 - Elsevier
The multi-layer perceptron architecture in PINNs model severely limits the model's ability to
learn the temporal evolution of equation features. Instead, the GRU network is capable of …
learn the temporal evolution of equation features. Instead, the GRU network is capable of …
Phase field smoothing-PINN: A neural network solver for partial differential equations with discontinuous coefficients
In this study, we propose a novel phase field smoothing-physics informed neural network
(PFS-PINN) approach to efficiently solve partial differential equations (PDEs) with …
(PFS-PINN) approach to efficiently solve partial differential equations (PDEs) with …
[HTML][HTML] Investigating neural networks with groundwater flow equation loss
Abstract Physics-Informed Neural Networks (PINNs) are considered a powerful tool for
solving partial differential equations (PDEs), particularly for the groundwater flow (GF) …
solving partial differential equations (PDEs), particularly for the groundwater flow (GF) …
Interface crack analysis in 2D bounded dissimilar materials using an enriched physics-informed neural networks
This study explores the application of physics-informed neural networks (PINNs) to analyze
interface crack problems within the context of elastic bimaterial fracture mechanics …
interface crack problems within the context of elastic bimaterial fracture mechanics …
Railway safety through predictive vertical displacement analysis using the PINN-EKF synergy
Abstract Scientific Machine Learning (SciML) finds extensive application in daily life,
industry, and scientific research. Specifically, in railway data analysis, it utilizes tools such as …
industry, and scientific research. Specifically, in railway data analysis, it utilizes tools such as …