mailitics

Tag: physics

  • Physics-informed Gaussian Process Regression in Solving Eigenvalue Problem of Linear Operators

    Physics-informed Gaussian Process Regression in Solving Eigenvalue Problem of Linear Operators arXiv:2601.06462v1 Announce Type: new Abstract: Applying Physics-Informed Gaussian Process Regression to the eigenvalue problem $(mathcal{L}-lambda)u = 0$ poses a fundamental challenge, where the null source term results in a trivial predictive mean and a degenerate marginal likelihood. Drawing inspiration from system identification, we construct…

  • Towards a Physics Foundation Model

    Towards a Physics Foundation Model arXiv:2509.13805v1 Announce Type: cross Abstract: Foundation models have revolutionized natural language processing through a “train once, deploy anywhere” paradigm, where a single pre-trained model adapts to countless downstream tasks without retraining. Access to a Physics Foundation Model (PFM) would be transformative — democratizing access to high-fidelity simulations, accelerating scientific discovery,…

  • Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)

    Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC) arXiv:2509.12401v1 Announce Type: cross Abstract: Physics-aware deep learning (PADL) has gained popularity for use in complex spatiotemporal dynamics (field evolution) simulations, such as those that arise frequently in computational modeling of energetic materials (EM). Here, we show that…

  • Physics-Informed Neural Networks for Inverse PDE Problems

    Physics-Informed Neural Networks for Inverse PDE Problems Solving the Heat Equation using DeepXDE. The post Physics-Informed Neural Networks for Inverse PDE Problems appeared first on Towards Data Science. Marco Hening Tallarico Go to original source

  • Physics-informed machine learning: A mathematical framework with applications to time series forecasting

    Physics-informed machine learning: A mathematical framework with applications to time series forecasting arXiv:2507.08906v1 Announce Type: new Abstract: Physics-informed machine learning (PIML) is an emerging framework that integrates physical knowledge into machine learning models. This physical prior often takes the form of a partial differential equation (PDE) system that the regression function must satisfy. In the…

  • Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecasting

    Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecasting arXiv:2506.08049v1 Announce Type: new Abstract: Subseasonal-to-seasonal (S2S) forecasting, which predicts climate conditions from several weeks to months in advance, presents significant challenges due to the chaotic dynamics of atmospheric systems and complex interactions across multiple scales. Current approaches often fail to explicitly model underlying physical processes and teleconnections…

  • When Physics Meets Finance: Using AI to Solve Black-Scholes

    When Physics Meets Finance: Using AI to Solve Black-Scholes DISCLAIMER: This is not financial advice. I’m a PhD in Aerospace Engineering with a strong focus on Machine Learning: I’m not a financial advisor. This article is intended solely to demonstrate the power of Physics-Informed Neural Networks (PINNs) in a financial context. When I was 16,…

  • An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation

    An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation arXiv:2503.00213v1 Announce Type: new Abstract: Physics-informed machine learning is one of the most commonly used methods for fusing physical knowledge in the form of partial differential equations with experimental data. The idea is to construct a loss function where the physical…