mailitics

Category: physics.comp-ph

  • Riemannian Stochastic Interpolants for Amorphous Particle Systems

    Riemannian Stochastic Interpolants for Amorphous Particle Systems arXiv:2512.16607v1 Announce Type: new Abstract: Modern generative models hold great promise for accelerating diverse tasks involving the simulation of physical systems, but they must be adapted to the specific constraints of each domain. Significant progress has been made for biomolecules and crystalline materials. Here, we address amorphous materials…

  • Self-adaptive weighting and sampling for physics-informed neural networks

    Self-adaptive weighting and sampling for physics-informed neural networks arXiv:2511.05452v1 Announce Type: new Abstract: Physics-informed deep learning has emerged as a promising framework for solving partial differential equations (PDEs). Nevertheless, training these models on complex problems remains challenging, often leading to limited accuracy and efficiency. In this work, we introduce a hybrid adaptive sampling and weighting…

  • Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces

    Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces arXiv:2511.03735v1 Announce Type: new Abstract: Designing frictional interfaces to exhibit prescribed macroscopic behavior is a challenging inverse problem, made difficult by the non-uniqueness of solutions and the computational cost of contact simulations. Traditional approaches rely on heuristic search over low-dimensional parameterizations, which limits…

  • Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes

    Bilevel optimization for learning hyperparameters: Application to solving PDEs and inverse problems with Gaussian processes arXiv:2510.05568v1 Announce Type: new Abstract: Methods for solving scientific computing and inference problems, such as kernel- and neural network-based approaches for partial differential equations (PDEs), inverse problems, and supervised learning tasks, depend crucially on the choice of hyperparameters. Specifically, the…

  • Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices

    Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices arXiv:2507.19663v1 Announce Type: new Abstract: Solder joint reliability related to failures due to thermomechanical loading is a critically important yet physically complex engineering problem. As a result, simulated behavior is oftentimes computationally expensive. In an increasingly data-driven world, the usage of efficient data-driven design…

  • Learning Enhanced Ensemble Filters

    Learning Enhanced Ensemble Filters arXiv:2504.17836v1 Announce Type: new Abstract: The filtering distribution in hidden Markov models evolves according to the law of a mean-field model in state–observation space. The ensemble Kalman filter (EnKF) approximates this mean-field model with an ensemble of interacting particles, employing a Gaussian ansatz for the joint distribution of the state and…

  • FEAT: Free energy Estimators with Adaptive Transport

    FEAT: Free energy Estimators with Adaptive Transport arXiv:2504.11516v1 Announce Type: new Abstract: We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation — a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled…

  • Density estimation via mixture discrepancy and moments

    Density estimation via mixture discrepancy and moments arXiv:2504.01570v1 Announce Type: new Abstract: With the aim of generalizing histogram statistics to higher dimensional cases, density estimation via discrepancy based sequential partition (DSP) has been proposed [D. Li, K. Yang, W. Wong, Advances in Neural Information Processing Systems (2016) 1099-1107] to learn an adaptive piecewise constant approximation…

  • Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond

    Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond arXiv:2502.04575v1 Announce Type: new Abstract: Given an unnormalized probability density $piproptomathrm{e}^{-V}$, estimating its normalizing constant $Z=int_{mathbb{R}^d}mathrm{e}^{-V(x)}mathrm{d}x$ or free energy $F=-log Z$ is a crucial problem in Bayesian statistics, statistical mechanics, and machine learning. It is challenging especially in high dimensions…

  • Explicit and data-Efficient Encoding via Gradient Flow

    Explicit and data-Efficient Encoding via Gradient Flow arXiv:2412.00864v1 Announce Type: new Abstract: The autoencoder model typically uses an encoder to map data to a lower dimensional latent space and a decoder to reconstruct it. However, relying on an encoder for inversion can lead to suboptimal representations, particularly limiting in physical sciences where precision is key.…