Cover Image

Physics-informed Deep Learning to Solve Three-dimensional Terzaghi Consolidation Equation: Forward and Inverse Problems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yuan, Biao, Heitor, Ana, Wang, He, Chen, Xiaohui
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computational Engineering, Finance, and Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
by: Behroozi, Abdolmehdi, et al.
Published: (2025)

Solving Differential Equations with Constrained Learning
by: Moro, Viggo, et al.
Published: (2024)

Learning Latent Hardening (LLH): Enhancing Deep Learning with Domain Knowledge for Material Inverse Problems
by: Tian, Qinyi, et al.
Published: (2025)

Physics-Informed Deep Contrast Source Inversion: A Unified Framework for Inverse Scattering Problems
by: Sun, Haoran, et al.
Published: (2025)

Physics-informed DeepONet with stiffness-based loss functions for structural response prediction
by: Ahmed, Bilal, et al.
Published: (2024)

Solving Inverse Problem for Multi-armed Bandits via Convex Optimization
by: Zhu, Hao, et al.
Published: (2025)

The Role of Deep Learning in Financial Asset Management: A Systematic Review
by: Reis, Pedro, et al.
Published: (2025)

Toward AI-Driven Digital Twins for Metropolitan Floods: A Conditional Latent Dynamics Network Surrogate of the Shallow Water Equations
by: Si, Phillip, et al.
Published: (2026)

A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction
by: Cheng, Yu, et al.
Published: (2025)

Large and Deep Factor Models
by: Kelly, Bryan, et al.
Published: (2024)

Physics-Informed Deep Learning Model for Line-integral Diagnostics Across Fusion Devices
by: Wang, Cong, et al.
Published: (2024)

Form-Finding and Physical Property Predictions of Tensegrity Structures Using Deep Neural Networks
by: Chen, Muhao, et al.
Published: (2024)

Physics-informed neural networks for parameter learning of wildfire spreading
by: Vogiatzoglou, Konstantinos, et al.
Published: (2024)

FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated Learning
by: Liu, Jiayu, et al.
Published: (2024)

Deep Reinforcement Learning for Modelling Protein Complexes
by: Gao, Ziqi, et al.
Published: (2024)

Storm Surge in Color: RGB-Encoded Physics-Aware Deep Learning for Storm Surge Forecasting
by: Zhao, Jinpai, et al.
Published: (2025)

Reservoir History Matching of the Norne field with generative exotic priors and a coupled Mixture of Experts -- Physics Informed Neural Operator Forward Model
by: Etienam, Clement, et al.
Published: (2024)

Deep Learning for One-dimensional Consolidation
by: Bekele, Yared W.
Published: (2020)

Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials
by: Yang, Yanyan, et al.
Published: (2024)

Differentiation Strategies for Acoustic Inverse Problems: Admittance Estimation and Shape Optimization
by: Borrel-Jensen, Nikolas, et al.
Published: (2025)

SpecBridge: Bridging Mass Spectrometry and Molecular Representations via Cross-Modal Alignment
by: Wang, Yinkai, et al.
Published: (2026)

The Enhanced Physics-Informed Kolmogorov-Arnold Networks: Applications of Newton's Laws in Financial Deep Reinforcement Learning (RL) Algorithms
by: Thoi, Trang, et al.
Published: (2026)

Deep Reinforcement Learning in Factor Investment
by: Liu, Junlin
Published: (2025)

Learning solutions of parametric Navier-Stokes with physics-informed neural networks
by: Naderibeni, M., et al.
Published: (2024)

Physics-Informed Neural Networks for Solving Contact Problems in Three Dimensions
by: Sahin, Tarik, et al.
Published: (2024)

Physics-informed solution reconstruction in elasticity and heat transfer using the explicit constraint force method
by: Rowan, Conor, et al.
Published: (2025)

Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis
by: Cheng, Qishuo, et al.
Published: (2024)

Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
by: Fang, Shikai, et al.
Published: (2023)

Modern, Efficient, and Differentiable Transport Equation Models using JAX: Applications to Population Balance Equations
by: Alsubeihi, Mohammed, et al.
Published: (2024)

A Cutting Mechanics-based Machine Learning Modeling Method to Discover Governing Equations of Machining Dynamics
by: Ren, Alisa, et al.
Published: (2025)

Inverse Reinforcement Learning via Convex Optimization
by: Zhu, Hao, et al.
Published: (2025)

Physics-informed active learning with simultaneous weak-form latent space dynamics identification
by: He, Xiaolong, et al.
Published: (2024)

Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects
by: Li, Dong, et al.
Published: (2026)

Extremization to Fine Tune Physics Informed Neural Networks for Solving Boundary Value Problems
by: Thiruthummal, Abhiram Anand, et al.
Published: (2024)

Derivative-enhanced Deep Operator Network
by: Qiu, Yuan, et al.
Published: (2024)

Interpretable long-term traffic modelling on national road networks using theory-informed deep learning
by: Li, Yue, et al.
Published: (2026)

Physics-informed Partitioned Coupled Neural Operator for Complex Networks
by: Wu, Weidong, et al.
Published: (2024)

Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls
by: Chen, Jungang, et al.
Published: (2023)

Parsimonious Universal Function Approximator for Elastic and Elasto-Plastic Cavity Expansion Problems
by: Chen, Xiao-Xuan, et al.
Published: (2024)

Physics-Informed Machine Learning for Smart Additive Manufacturing
by: Sharma, Rahul, et al.
Published: (2024)