mailitics

Tag: inverse

  • Estimation of instrument and noise parameters for inverse problem based on prior diffusion model

    Estimation of instrument and noise parameters for inverse problem based on prior diffusion model arXiv:2602.11711v1 Announce Type: new Abstract: This article addresses the issue of estimating observation parameters (response and error parameters) in inverse problems. The focus is on cases where regularization is introduced in a Bayesian framework and the prior is modeled by a…

  • Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing

    Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing arXiv:2512.02495v1 Announce Type: new Abstract: Inverse problems arise across scientific and engineering domains, where the goal is to infer hidden parameters or physical fields from indirect and noisy observations. Classical approaches, such as variational regularization and Bayesian inference, provide well established theoretical…

  • 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…

  • PBPK-iPINNs : Inverse Physics-Informed Neural Networks for Physiologically Based Pharmacokinetic Brain Models

    PBPK-iPINNs : Inverse Physics-Informed Neural Networks for Physiologically Based Pharmacokinetic Brain Models arXiv:2509.12666v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) leverage machine learning with differential equations to solve direct and inverse problems, ensuring predictions follow physical laws. Physiologically based pharmacokinetic (PBPK) modeling advances beyond classical compartmental approaches by using a mechanistic, physiology focused framework.…

  • Improved Inference of Inverse Ising Problems under Missing Observations in Restricted Boltzmann Machines

    Improved Inference of Inverse Ising Problems under Missing Observations in Restricted Boltzmann Machines arXiv:2504.05643v1 Announce Type: new Abstract: Restricted Boltzmann machines (RBMs) are energy-based models analogous to the Ising model and are widely applied in statistical machine learning. The standard inverse Ising problem with a complete dataset requires computing both data and model expectations and…

  • Gradient-Based Non-Linear Inverse Learning

    Gradient-Based Non-Linear Inverse Learning arXiv:2412.16794v1 Announce Type: new Abstract: We study statistical inverse learning in the context of nonlinear inverse problems under random design. Specifically, we address a class of nonlinear problems by employing gradient descent (GD) and stochastic gradient descent (SGD) with mini-batching, both using constant step sizes. Our analysis derives convergence rates for…