Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations
Fuente:
arXiv
Saved in:
| Main Authors: | Rezaei, Shahed, Moeineddin, Ahmad, Kaliske, Michael, Apel, Markus |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs
by: Rezaei, Shahed, et al.
Published: (2024)
by: Rezaei, Shahed, et al.
Published: (2024)
A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations
by: Rezaei, Shahed, et al.
Published: (2024)
by: Rezaei, Shahed, et al.
Published: (2024)
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
by: Yamazaki, Yusuke, et al.
Published: (2024)
by: Yamazaki, Yusuke, et al.
Published: (2024)
A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: efficient mapping of concrete microstructures to its fracture properties
by: Koopas, Rasoul Najafi, et al.
Published: (2024)
by: Koopas, Rasoul Najafi, et al.
Published: (2024)
Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space
by: Koopas, Rasoul Najafi, et al.
Published: (2024)
by: Koopas, Rasoul Najafi, et al.
Published: (2024)
Tackling multiphysics problems via finite element-guided physics-informed operator learning
by: Yamazaki, Yusuke, et al.
Published: (2026)
by: Yamazaki, Yusuke, et al.
Published: (2026)
Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference
by: McQuarrie, Shane A., et al.
Published: (2025)
by: McQuarrie, Shane A., et al.
Published: (2025)
RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations
by: Ouyang, Weihang, et al.
Published: (2025)
by: Ouyang, Weihang, et al.
Published: (2025)
Simulation of parametrized cardiac electrophysiology in three dimensions using physics-informed neural networks
by: Gomez, Roshan Antony, et al.
Published: (2025)
by: Gomez, Roshan Antony, et al.
Published: (2025)
Learning solutions of parametric Navier-Stokes with physics-informed neural networks
by: Naderibeni, M., et al.
Published: (2024)
by: Naderibeni, M., et al.
Published: (2024)
A brief review of the Deep BSDE method for solving high-dimensional partial differential equations
by: Han, Jiequn, et al.
Published: (2025)
by: Han, Jiequn, et al.
Published: (2025)
A parametric framework for kernel-based dynamic mode decomposition using deep learning
by: Kevopoulos, Konstantinos, et al.
Published: (2024)
by: Kevopoulos, Konstantinos, et al.
Published: (2024)
Local learning for stable backpropagation-free neural network training towards physical learning
by: Guo, Yaqi, et al.
Published: (2026)
by: Guo, Yaqi, et al.
Published: (2026)
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)
by: Chen, Jungang, et al.
Published: (2023)
Physics-informed neural networks for parameter learning of wildfire spreading
by: Vogiatzoglou, Konstantinos, et al.
Published: (2024)
by: Vogiatzoglou, Konstantinos, et al.
Published: (2024)
Utilising physics-guided deep learning to overcome data scarcity
by: Bai, Jinshuai, et al.
Published: (2022)
by: Bai, Jinshuai, et al.
Published: (2022)
Modular parametric PGD enabling online solution of partial differential equations
by: Pasquale, Angelo, et al.
Published: (2024)
by: Pasquale, Angelo, et al.
Published: (2024)
COMMET: orders-of-magnitude speed-up in finite element method via batch-vectorized neural constitutive updates
by: Alheit, Benjamin, et al.
Published: (2025)
by: Alheit, Benjamin, et al.
Published: (2025)
Interpretable long-term traffic modelling on national road networks using theory-informed deep learning
by: Li, Yue, et al.
Published: (2026)
by: Li, Yue, et al.
Published: (2026)
Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties
by: Liu, Ning, et al.
Published: (2024)
by: Liu, Ning, et al.
Published: (2024)
Aerodynamic force reconstruction using physics-informed Gaussian processes
by: Tondo, Gledson Rodrigo, et al.
Published: (2026)
by: Tondo, Gledson Rodrigo, et al.
Published: (2026)
Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators
by: Ouyang, Weihang, et al.
Published: (2025)
by: Ouyang, Weihang, et al.
Published: (2025)
Constitutive description of snow at finite strains by the modified cam‐clay model and an implicit gradient damage formulation
by: Ahmad Moeineddin, et al.
Published: (2024)
by: Ahmad Moeineddin, et al.
Published: (2024)
Physics-informed active learning with simultaneous weak-form latent space dynamics identification
by: He, Xiaolong, et al.
Published: (2024)
by: He, Xiaolong, et al.
Published: (2024)
Thermodynamically consistent machine learning model for excess Gibbs energy
by: Hoffmann, Marco, et al.
Published: (2025)
by: Hoffmann, Marco, et al.
Published: (2025)
Noise-robust multi-fidelity surrogate modelling for parametric partial differential equations
by: Kent, Benjamin M., et al.
Published: (2025)
by: Kent, Benjamin M., et al.
Published: (2025)
Multifidelity linear regression for scientific machine learning from scarce data
by: Qian, Elizabeth, et al.
Published: (2024)
by: Qian, Elizabeth, 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)
by: Rowan, Conor, et al.
Published: (2025)
Implicit differentiation with second-order derivatives and benchmarks in finite-element-based differentiable physics
by: Xue, Tianju
Published: (2025)
by: Xue, Tianju
Published: (2025)
Beyond development: Challenges in deploying machine learning models for structural engineering applications
by: Esteghamati, Mohsen Zaker, et al.
Published: (2024)
by: Esteghamati, Mohsen Zaker, et al.
Published: (2024)
MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning
by: Hoffmann, Marco, et al.
Published: (2025)
by: Hoffmann, Marco, et al.
Published: (2025)
A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites
by: Bahtiri, Betim, et al.
Published: (2024)
by: Bahtiri, Betim, et al.
Published: (2024)
Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes
by: Würth, Tobias, et al.
Published: (2024)
by: Würth, Tobias, et al.
Published: (2024)
A quantitative analysis of knowledge-learning preferences in large language models in molecular science
by: Liu, Pengfei, et al.
Published: (2024)
by: Liu, Pengfei, et al.
Published: (2024)
A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content
by: Bahtiri, Betim, et al.
Published: (2023)
by: Bahtiri, Betim, et al.
Published: (2023)
Some variation of COBRA in sequential learning setup
by: Bhambu, Aryan, et al.
Published: (2024)
by: Bhambu, Aryan, et al.
Published: (2024)
A Spectral-based Physics-informed Finite Operator Learning for Prediction of Mechanical Behavior of Microstructures
by: Harandi, Ali, et al.
Published: (2024)
by: Harandi, Ali, et al.
Published: (2024)
Operator-based machine learning framework for generalizable prediction of unsteady treatment dynamics in stormwater infrastructure
by: Shatarah, Mohamed, et al.
Published: (2025)
by: Shatarah, Mohamed, et al.
Published: (2025)
Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points
by: Esders, Malte, et al.
Published: (2023)
by: Esders, Malte, et al.
Published: (2023)
Model-based deep reinforcement learning for accelerated learning from flow simulations
by: Weiner, Andre, et al.
Published: (2024)
by: Weiner, Andre, et al.
Published: (2024)
Similar Items
-
Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs
by: Rezaei, Shahed, et al.
Published: (2024) -
A finite operator learning technique for mapping the elastic properties of microstructures to their mechanical deformations
by: Rezaei, Shahed, et al.
Published: (2024) -
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
by: Yamazaki, Yusuke, et al.
Published: (2024) -
A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: efficient mapping of concrete microstructures to its fracture properties
by: Koopas, Rasoul Najafi, et al.
Published: (2024) -
Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space
by: Koopas, Rasoul Najafi, et al.
Published: (2024)