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We propose a method for variable selection in the intensity function of spatial point processes that combines sparsity-promoting estimation with noise-robust model selection. As high-resolution spatial data becomes increasingly available…

Methodology · Statistics 2025-10-30 Dominik Sturm , Ivo F. Sbalzarini

Biomolecular force fields have been traditionally derived based on a mixture of reference quantum chemistry data and experimental information obtained on small fragments. However, the possibility to run extensive molecular dynamics…

Computational Physics · Physics 2020-06-19 Thorben Fröhlking , Mattia Bernetti , Nicola Calonaci , Giovanni Bussi

Understanding and predicting the dynamical properties of systems involving dry friction is a major concern in physics and engineering. It abounds in many mechanical processes, from the sound produced by a violin to the screeching of chalk…

Probability · Mathematics 2023-05-16 Josselin Garnier , Ziyu Lu , Laurent Mertz

We use machine learning models to predict ion density and electron temperature from visible emission spectra, in a high energy density pulsed-power-driven aluminum plasma, generated by an exploding wire array. Radiation transport…

Plasma Physics · Physics 2023-09-01 Rishabh Datta , Faez Ahmed , Jack D Hare

An analytical model is presented for the description of nonlinear dust-ion-acoustic waves propagating in an unmagnetized, collisionless, three component plasma composed of electrons, ions and inertial dust grains. The formulation relies on…

Plasma Physics · Physics 2009-11-10 I. Kourakis , P. K. Shukla

In model-free deep reinforcement learning (RL) algorithms, using noisy value estimates to supervise policy evaluation and optimization is detrimental to the sample efficiency. As this noise is heteroscedastic, its effects can be mitigated…

Machine Learning · Computer Science 2022-05-04 Vincent Mai , Kaustubh Mani , Liam Paull

The operation of power systems is affected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation and weather-dependent renewables introduce power…

Systems and Control · Electrical Eng. & Systems 2022-11-04 Johannes Kruse , Eike Cramer , Benjamin Schäfer , Dirk Witthaut

Uncertainty estimation for unlabeled data is crucial to active learning. With a deep neural network employed as the backbone model, the data selection process is highly challenging due to the potential over-confidence of the model…

Machine Learning · Computer Science 2024-02-14 Xingjian Li , Pengkun Yang , Yangcheng Gu , Xueying Zhan , Tianyang Wang , Min Xu , Chengzhong Xu

Learning and forecasting stochastic time series is essential in various scientific fields. However, despite the proposals of nonlinear filters and deep-learning methods, it remains challenging to capture nonlinear dynamics from a few noisy…

Methodology · Statistics 2025-02-21 Christian Donner , Anuj Mishra , Hideaki Shimazaki

Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, efficient collection before thermalization remains an obstacle for…

Mesoscale and Nanoscale Physics · Physics 2023-07-19 Adela Habib , Nicholas Lubbers , Sergei Tretiak , Benjamin Nebgen

We use machine learning (ML) to infer stress and plastic flow rules using data from repre- sentative polycrystalline simulations. In particular, we use so-called deep (multilayer) neural networks (NN) to represent the two response…

Computational Physics · Physics 2018-09-05 Reese E. Jones , Jeremy A. Templeton , Clay M. Sanders , Jakob T. Ostien

In many areas of science and engineering, discovering the governing differential equations from the noisy experimental data is an essential challenge. It is also a critical step in understanding the physical phenomena and prediction of the…

Methodology · Statistics 2026-05-12 Jiuhai Chen , Lulu Kang , Guang Lin

We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…

High Energy Physics - Phenomenology · Physics 2018-09-19 Johann Brehmer , Kyle Cranmer , Gilles Louppe , Juan Pavez

The paper presents a stochastic approach to estimate the aerodynamic forces with local dynamics on wind turbine blades in unsteady wind inflow. This is done by integrating a stochastic model of lift and drag dynamics for an airfoil into the…

Fluid Dynamics · Physics 2015-06-12 Muhammad Ramzan Luhur , Joachim Peinke , Martin Kühn , Matthias Wächter

Machine learned chemical potentials have shown great promise as alternatives to conventional computational chemistry methods to represent the potential energy of a given atomic or molecular system as a function of its geometry. However,…

Chemical Physics · Physics 2023-11-15 Christian Devereux , Yoona Yang , Carles Martí , Judit Zádor , Michael S. Eldred , Habib N. Najm

In this paper, we present a machine learning-based data generator framework tailored to aid researchers who utilize simulations to examine various physical systems or processes. High computational costs and the resulting limited data often…

Machine Learning · Computer Science 2023-05-17 Sabber Ahamed , Md Mesbah Uddin

Many tasks in explainable machine learning, such as data valuation and feature attribution, perform expensive computation for each data point and are intractable for large datasets. These methods require efficient approximations, and…

Machine Learning · Computer Science 2024-10-31 Ian Covert , Chanwoo Kim , Su-In Lee , James Zou , Tatsunori Hashimoto

While offering unprecedented resolution of atomic and electronic structure, Scanning Probe Microscopy techniques have found greater challenges in providing reliable electrostatic characterization at the same scale. In this work, we…

In the presence of system-environment coupling, classical complex systems undergo stochastic dynamics, where rich phenomena can emerge at large spatio-temporal scales. To investigate these phenomena, numerical approaches for simulating…

Statistical Mechanics · Physics 2024-03-15 Pei-Fang Wu , Wei-Chen Guo , Liang He

Sparse regression has recently emerged as an attractive approach for discovering models of spatiotemporally complex dynamics directly from data. In many instances, such models are in the form of nonlinear partial differential equations…

Dynamical Systems · Mathematics 2020-01-29 Patrick A. K. Reinbold , Daniel R. Gurevich , Roman O. Grigoriev