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In pattern classification, polynomial classifiers are well-studied methods as they are capable of generating complex decision surfaces. Unfortunately, the use of multivariate polynomials is limited to kernels as in support vector machines,…

Machine Learning · Computer Science 2017-11-07 Zhongming Chen , Kim Batselier , Johan A. K. Suykens , Ngai Wong

One of basic difficulties of machine learning is handling unknown rotations of objects, for example in image recognition. A related problem is evaluation of similarity of shapes, for example of two chemical molecules, for which direct…

Machine Learning · Computer Science 2018-01-04 Jarek Duda

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

We introduce a novel equivariant graph neural network (GNN) architecture designed to predict the tensorial response properties of molecules. Unlike traditional frameworks that focus on regressing scalar quantities and derive tensorial…

Machine Learning · Computer Science 2025-11-11 Jean Philip Filling , Felix Post , Michael Wand , Denis Andrienko

Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of…

Chemical Physics · Physics 2026-02-24 Valerii Andreichev , Jindra Dušek , Markus Meuwly , Jeremy O. Richardson

The next generation of force fields for molecular dynamics will be developed using a wealth of data. Training systematically with experimental data remains a challenge, however, especially for machine learning potentials. Differentiable…

Biomolecules · Quantitative Biology 2025-04-16 Joe G Greener

Multivariate time series have many applications, from healthcare and meteorology to life science. Although deep learning models have shown excellent predictive performance for time series, they have been criticised for being "black-boxes"…

Machine Learning · Computer Science 2024-05-06 Qiqi Su , Christos Kloukinas , Artur d'Avila Garcez

We develop a Machine Learning Inversion method for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer…

Soft Condensed Matter · Physics 2025-11-21 Lijie Ding , Chi-Huan Tung , Bobby G. Sumpter , Wei-Ren Chen , Changwoo Do

The resemblance between the methods used in quantum-many body physics and in machine learning has drawn considerable attention. In particular, tensor networks (TNs) and deep learning architectures bear striking similarities to the extent…

Machine Learning · Statistics 2019-08-05 Ding Liu , Shi-Ju Ran , Peter Wittek , Cheng Peng , Raul Blázquez García , Gang Su , Maciej Lewenstein

The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations. In many cases,…

Machine Learning · Computer Science 2026-03-27 Michelangelo Domina , Joseph William Abbott , Paolo Pegolo , Filippo Bigi , Michele Ceriotti

Deep learning has been the most popular machine learning method in the last few years. In this chapter, we present the application of deep learning and physics-informed neural networks concerning structural mechanics and vibration problems.…

Machine Learning · Computer Science 2022-02-23 Ehsan Haghighat , Ali Can Bekar , Erdogan Madenci , Ruben Juanes

Using machine learning, we explore the utility of various deep neural networks (NN) when applied to high harmonic generation (HHG) scenarios. First, we train the NNs to predict the time-dependent dipole and spectra of HHG emission from…

Optics · Physics 2023-03-07 M. Lytova , M. Spanner , I. Tamblyn

Temporal interpolation often plays a crucial role to learn meaningful representations in dynamic scenes. In this paper, we propose a novel method to train spatiotemporal neural radiance fields of dynamic scenes based on temporal…

Computer Vision and Pattern Recognition · Computer Science 2023-03-30 Sungheon Park , Minjung Son , Seokhwan Jang , Young Chun Ahn , Ji-Yeon Kim , Nahyup Kang

Discrete-time modeling of acoustic, mechanical and electrical systems is a prominent topic in the musical signal processing literature. Such models are mostly derived by discretizing a mathematical model, given in terms of ordinary or…

Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo…

Computational Physics · Physics 2020-11-12 Anjana M. Samarakoon , D. Alan Tennant

Molecular property prediction constitutes a cornerstone of drug discovery and materials science, necessitating models capable of disentangling complex structure-property relationships across diverse molecular modalities. Existing approaches…

Machine Learning · Computer Science 2026-03-24 Long Xu , Junping Guo , Jianbo Zhao , Jianbo Lu , Yuzhong Peng

The expression for polarized electric dipole moment of well-deformed reflection asymmetric nuclei is obtained in the framework of liquid-drop model in the case of geometrically similar proton and neutron surfaces. The expression for…

Nuclear Theory · Physics 2015-05-28 V. Yu. Denisov

Particle Accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization…

Machine Learning · Computer Science 2025-04-08 Kishansingh Rajput , Sen Lin , Auralee Edelen , Willem Blokland , Malachi Schram

We introduce property-independent kernels for machine learning modeling of arbitrarily many molecular properties. The kernels encode molecular structures for training sets of varying size, as well as similarity measures sufficiently diffuse…

Chemical Physics · Physics 2015-03-18 Raghunathan Ramakrishnan , O. Anatole von Lilienfeld

Deep neural networks are used to model the magnetization dynamics in magnetic thin film elements. The magnetic states of a thin film element can be represented in a low dimensional space. With convolutional autoencoders a compression ratio…