Physics-informed machine learning

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

5 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 63 papers with code (192 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
  17 Dec 2019 4 repositories listed
  The lifting map is applied to data obtained by evaluating a model for the original nonlinear system.
• Physics-informed neural networks for highly compressible flows
  15 Sep 2023 2 repositories listed
  This thesis shows that physics-informed neural networks struggle with highly compressible problems for two independent reasons.
• Multi-Objective Loss Balancing for Physics-Informed Deep Learning
  19 Oct 2021 2 repositories listed
  Physics-Informed Neural Networks (PINN) are algorithms from deep learning leveraging physical laws by including partial differential equations together with a respective set of boundary and initial conditions as penalty…
• A physics-informed neural network for wind turbine main bearing fatigue
  5 May 2020 2 repositories listed
  Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss).
• Physics-informed neural networks for corrosion-fatigue prognosis
  22 Sep 2019 2 repositories listed
  The result is a cumulative damage model where the physics-informed layers are used to model the relatively well understood physics (crack growth through Paris law) and the data-driven layers account for the hard to…
• A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data
  27 May 2025 1 repository listed
  Furthermore, we modify the aggregation step of message-passing such that it is aware of shocks and discontinuities, resulting in sharper reconstructions of these features.
• Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approach
  19 May 2025 1 repository listed
  In conventional PIML, physical information is typically incorporated by constructing a hybrid loss function that combines data-driven loss and physics loss through linear scalarization.
• LaPON: A Lagrange's-mean-value-theorem-inspired operator network for solving PDEs and its application on NSE
  18 May 2025 1 repository listed
  Accelerating the solution of nonlinear partial differential equations (PDEs) while maintaining accuracy at coarse spatiotemporal resolution remains a key challenge in scientific computing.
• A Statistical Evaluation of Indoor LoRaWAN Environment-Aware Propagation for 6G: MLR, ANOVA, and Residual Distribution Analysis
  23 Apr 2025 1 repository listed
  Modeling path loss in indoor LoRaWAN technology deployments is inherently challenging due to structural obstructions, occupant density and activities, and fluctuating environmental conditions.
• NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
  20 Mar 2025 1 repository listed
  This work discusses the methodology and performance of NeuralFoil with several case studies, including a practical airfoil design optimization study including both aerodynamic and non-aerodynamic constraints.
• QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs
  20 Mar 2025 1 repository listed
  Our results demonstrate that the QCPINN achieves stable convergence and comparable accuracy, while requiring approximately 10% of the trainable parameters used in classical approaches.
• PDE-DKL: PDE-constrained deep kernel learning in high dimensionality
  30 Jan 2025 1 repository listed
  This synergy unifies the strengths of both NNs and GPs, yielding high accuracy, robust uncertainty estimates, and computational efficiency for high-dimensional PDEs.
• DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series Analysis
  1 Dec 2024 1 repository listed
  Time series forecasting is a crucial yet challenging task in machine learning, requiring domain-specific knowledge due to its wide-ranging applications.
• RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors
  27 Nov 2024 1 repository listed
  Electricity grid's resiliency and climate change strongly impact one another due to an array of technical and policy-related decisions that impact both.
• Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Models
  18 Nov 2024 1 repository listed
  In both applications, our method outperforms both conventional neural network based approaches as-well as state-of-the-art Physics Informed Machine Learning methods.
• Physics-informed kernel learning
  20 Sep 2024 1 repository listed
  Building on the formulation of the problem as a kernel regression task, we use Fourier methods to approximate the associated kernel, and propose a tractable estimator that minimizes the physics-informed risk function.
• Non-overlapping, Schwarz-type Domain Decomposition Method for Physics and Equality Constrained Artificial Neural Networks
  20 Sep 2024 1 repository listed
  We present a non-overlapping, Schwarz-type domain decomposition method with a generalized interface condition, designed for physics-informed machine learning of partial differential equations (PDEs) in both forward and…
• Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks
  24 Jul 2024 1 repository listed Syntology ran 4 of 4 samples · 0 unverified
  Physics-Informed Neural Networks (PINNs) have emerged as a robust framework for solving Partial Differential Equations (PDEs) by approximating their solutions via neural networks and imposing physics-based constraints…
• Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning
  21 Jul 2024 1 repository listed
  This method faces significant computational challenges, primarily due to the curse of dimensionality, as the computational cost increases exponentially with finer discretization.
• Deep Learning Evidence for Global Optimality of Gerver's Sofa
  15 Jul 2024 1 repository listed
  Our second approach is via discrete optimization of the Kallus-Romik upper bound, which converges to the maximum sofa area from above as the number of rotation angles increases.
• Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain Hyperelasticity
  15 Jul 2024 1 repository listed
  The present study aims to extend the novel physics-informed machine learning approach, specifically the neural-integrated meshfree (NIM) method, to model finite-strain problems characterized by nonlinear elasticity and…
• Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEs
  4 Jul 2024 1 repository listed
  We can parametrically solve partial differential equations in a data-free manner and provide accurate sensitivities, meaning the derivatives of the solution space with respect to the design space.
• Differentiable Predictive Control for Large-Scale Urban Road Networks
  14 Jun 2024 1 repository listed
  Transportation is a major contributor to CO2 emissions, making it essential to optimize traffic networks to reduce energy-related emissions.
• Adapting Physics-Informed Neural Networks to Improve ODE Optimization in Mosquito Population Dynamics
  7 Jun 2024 1 repository listed
  In this research, we propose a PINN framework with several improvements for forward and inverse problems for ODE systems with a case study application in modelling the dynamics of mosquito populations.
• Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine Learning
  1 Jun 2024 1 repository listed
  Manufacturing complexities and uncertainties have impeded the transition from material prototypes to commercial batteries, making prototype verification critical to quality assessment.
• Physics-informed Discretization-independent Deep Compositional Operator Network
  21 Apr 2024 1 repository listed
  To address this challenge, physics-informed training can offer a cost-effective strategy.
• Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs
  26 Mar 2024 1 repository listed
  We compare our approach to a similar CUDA implementation for MLPs and show that our implementation on the Intel Data Center GPU outperforms the CUDA implementation on Nvidia's H100 GPU by a factor up to 2.
• Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability
  15 Mar 2024 1 repository listed
  Here, we explore the application of an open-source pre-trained NN model, GlassNet, that can predict the characteristic temperatures necessary to compute glass stability (GS) and assess the feasibility of using these…
• Kolmogorov n-Widths for Multitask Physics-Informed Machine Learning (PIML) Methods: Towards Robust Metrics
  16 Feb 2024 1 repository listed
  This topic encompasses a broad array of methods and models aimed at solving a single or a collection of PDE problems, called multitask learning.
• Physics-informed machine learning as a kernel method
  12 Feb 2024 1 repository listed
  In this context, we consider a general regression problem where the empirical risk is regularized by a partial differential equation that quantifies the physical inconsistency.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.