mlsanityData to 14 Oct 2026

IDRLnet: A Physics-Informed Neural Network Library

arXiv 2107.04320 ↗First posted 9 Jul 2021cs.LG cs.NA math.NAcited by 10

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Abstract (arXiv)

Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems modeled by Partial Differential Equations (PDEs). This paper introduces IDRLnet, a Python toolbox for modeling and solving problems through PINN systematically. IDRLnet constructs the framework for a wide range of PINN algorithms and applications. It provides a structured way to incorporate geometric objects, data sources, artificial neural networks, loss metrics, and optimizers within Python. Furthermore, it provides functionality to solve noisy inverse problems, variational minimization, and integral differential equations. New PINN variants can be integrated into the framework easily. Source code, tutorials, and documentation are available at urlhttps://github.com/idrl-lab/idrlnet.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 12 of its 27 references that are in the map. First posted on arXiv 9 Jul 2021.

Builds on

12 of 27 references.

References · 12

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2019DeepXDE: A deep learning library for solving differential equationscited by 619
  3. 2021Physics-informed neural networks (PINNs) for fluid mechanics: A reviewcited by 473
  4. 2021Physics-informed neural networks with hard constraints for inverse designcited by 280
  5. 2020Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networkscited by 123
  6. 2020NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation frameworkcited by 58
  7. 2020Deep neural networks for the evaluation and design of photonic devicescited by 50
  8. 2020A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometriescited by 44
  9. 2018Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Datacited by 36
  10. 2020A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applicationscited by 33
  11. 2019PyDEns: a Python Framework for Solving Differential Equations with Neural Networkscited by 14
  12. 2021Elvet -- a neural network-based differential equation and variational problem solvercited by 7

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 3; 2025: 3.

10 papers.

  1. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
  2. 2024From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learningcited by 73
  3. 2022RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networkscited by 14
  4. 2025Physics-informed neural network solves minimal surfaces in curved spacetimecited by 6
  5. 2024Discovering Physics-Informed Neural Networks Model for Solving Partial Differential Equations through Evolutionary Computationcited by 5
  6. 2022Robust Regression with Highly Corrupted Data via Physics Informed Neural Networkscited by 4
  7. 2023Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colabcited by 0
  8. 2024jinns: a JAX Library for Physics-Informed Neural Networkscited by 0
  9. 2025Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigmcited by 0
  10. 2025Fundamentals of Regressioncited by 0