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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

The first step to realize automatic experimental data analysis for fusion plasma experiments is fitting noisy data of temperature and density spatial profiles, which are obtained routinely. However, it has been difficult to construct…

Plasma Physics · Physics 2019-07-24 Keisuke Fujii , Chihiro Suzuki , Masahiro Hasuo

We present the development and applications of a quadratic Spectral Neighbor Analysis Potential (q-SNAP) for ferromagnetic cobalt. Trained on Density Functional Theory calculations using the Perdew-Burke-Ernzerhof (DFT-PBE) functional, this…

Materials Science · Physics 2024-11-05 Marthe Bideault , Jérôme Creuze , Ryoji Asahi , Erich Wimmer

Nonlinear frequency response analysis is a widely used method for determining system dynamics in the presence of nonlinearities. In dusty plasmas, the plasma-grain interaction (e.g., grain charging fluctuations) can be characterized by a…

Plasma Physics · Physics 2020-10-26 Zhiyue Ding , Lorin S. Matthews , Truell W. Hyde

The ability to predict future events or patterns based on previous experience is crucial for many applications such as traffic control, weather forecasting, or supply chain management. While modern supervised Machine Learning approaches…

Neurons and Cognition · Quantitative Biology 2024-10-16 Florian Feiler , Emre Neftci , Younes Bouhadjar

We propose a semiparametric model for autonomous nonlinear dynamical systems and devise an estimation procedure for model fitting. This model incorporates subject-specific effects and can be viewed as a nonlinear semiparametric mixed…

Applications · Statistics 2011-12-01 Debashis Paul , Jie Peng , Prabir Burman

Establishing a mapping between nanocatalysts structure and their catalytic properties is essential for efficient design. To this end, we demonstrate the accuracy of a general machine learning framework on a representative and challenging…

Mesoscale and Nanoscale Physics · Physics 2025-09-16 Sofia Zinzani , Francesca Baletto , Kevin Rossi

A surrogate model for particle-in-cell plasma simulations based on a graph neural network is presented. The graph is constructed in such a way as to enable the representation of electromagnetic fields on a fixed spatial grid. The model is…

Plasma Physics · Physics 2025-03-04 Marin Mlinarević , George K. Holt , Adriano Agnello

We propose a novel approach for constructing training databases for Machine-Learned Interatomic Potential (MLIP) models, specifically designed to capture phase properties across a wide range of conditions. The framework is uniquely…

Materials Science · Physics 2025-12-03 Vincent G. Fletcher , Albert P. Bartók , Livia B. Pártay

Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters…

Machine Learning · Statistics 2019-04-01 Stephan Rasp , Sebastian Lerch

Foundational Machine Learning Potentials can resolve the accuracy and transferability limitations of classical force fields. They enable microscopic insights into material behavior through Molecular Dynamics simulations, which can crucially…

Computational Physics · Physics 2025-12-04 Paul Fuchs , Julija Zavadlav

The availability of property data is one of the major bottlenecks in the development of chemical processes, often requiring time-consuming and expensive experiments or limiting the design space to a small number of known molecules. This…

Chemical Physics · Physics 2023-09-22 Benedikt Winter , Clemens Winter , Timm Esper , Johannes Schilling , André Bardow

The analysis of the size of nanoparticles is an essential task in plasma technology and dusty plasmas. Light scattering techniques, based on Mie theory, can be used as a non-invasive and in-situ diagnostic tool for this purpose. However,…

Plasma Physics · Physics 2025-10-03 Alexander Schmitz , Andreas Petersen , Franko Greiner

All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and…

Materials Science · Physics 2024-03-21 Stephen R. Xie , Matthias Rupp , Richard G. Hennig

The limited extrapolative power of structure-based machine learning (ML) models is a critical bottleneck in chemical discovery, particularly for industrial R&D, where navigating uncharted chemical space to find next-generation materials or…

Accurate models are essential for design, performance prediction, control, and diagnostics in complex engineering systems. Physics-based models excel during the design phase but often become outdated during system deployment due to changing…

Machine Learning · Computer Science 2025-01-22 Zihan Liu , Prashant N. Kambali , C. Nataraj

We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks, it is predominantly used to just predict a single number. This…

Machine Learning · Statistics 2020-04-02 Beate Sick , Torsten Hothorn , Oliver Dürr

We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algorithm and the features de-entanglement method. We…

Materials Science · Physics 2022-05-24 Wenkai Fu , Steven R. Spurgeon , Chongmin Wang , Yuyan Shao , Wei Wang , Amra Peles

We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection…

High Energy Physics - Phenomenology · Physics 2024-10-24 Jai Bardhan , Tanumoy Mandal , Subhadip Mitra , Cyrin Neeraj , Monalisa Patra

Performance prediction, the task of estimating a system's performance without performing experiments, allows us to reduce the experimental burden caused by the combinatorial explosion of different datasets, languages, tasks, and models. In…

Computation and Language · Computer Science 2021-02-11 Zihuiwen Ye , Pengfei Liu , Jinlan Fu , Graham Neubig