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Existing curved-layer-based process planning methods for multi-axis manufacturing address collisions only indirectly and generate toolpaths in a post-processing step, leaving toolpath geometry uncontrolled during optimization. We present an…

Robotics · Computer Science 2026-04-22 Neelotpal Dutta , Tianyu Zhang , Tao Liu , Yongxue Chen , Charlie C. L. Wang

Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. Especially when the latter is not…

Machine Learning · Statistics 2018-12-31 Yanning Shen , Tianyi Chen , Georgios B. Giannakis

An effective approach for solving the three-dimensional Dirac equation for spherically symmetric local interactions, which we have introduced recently, is reviewed and consolidated. The merit of the approach is in producing Schrodinger-like…

Mathematical Physics · Physics 2009-11-07 A. D. Alhaidari

The numerical emulation of quantum systems often requires an exponential number of degrees of freedom which translates to a computational bottleneck. Methods of machine learning have been used in adjacent fields for effective feature…

Disordered Systems and Neural Networks · Physics 2020-08-10 A Berezutskii , M Beketov , D Yudin , Z Zimborás , J Biamonte

In this work, we study the existence of various classes of standing waves for a nonlinear Schr\"odinger system with quadratic interaction, along with a harmonic or partially harmonic potential. We establish the existence of ground-state…

Analysis of PDEs · Mathematics 2025-02-18 Vicente Alvarez , Amin Esfahani

In this article we demonstrate the applications of classical and quantum machine learning in quantum transport and spintronics. With the help of a two-terminal device with magnetic impurity we show how machine learning algorithms can…

Mesoscale and Nanoscale Physics · Physics 2023-03-28 Kumar Ghosh , Sumit Ghosh

Multi-group classification arises in many prediction and decision-making problems, including applications in epidemiology, genomics, finance, and image recognition. Although classification methods have advanced considerably, much of the…

Methodology · Statistics 2026-01-12 Yuchao Wang , Tianying Wang

Likelihood profiling is an efficient and powerful frequentist approach for parameter estimation, uncertainty quantification and practical identifiablity analysis. Unfortunately, these methods cannot be easily applied for stochastic models…

Spherically-symmetric atom-centered descriptors of atomic environments have been widely used for constructing potential or free energy surfaces of atomistic and colloidal systems and to characterize local structures using machine learning…

Soft Condensed Matter · Physics 2022-07-27 Gerardo Campos-Villalobos , Giuliana Giunta , Susana Marín-Aguilar , Marjolein Dijkstra

Multi-task learning has emerged as a powerful machine learning paradigm for integrating data from multiple sources, leveraging similarities between tasks to improve overall model performance. However, the application of multi-task learning…

Methodology · Statistics 2024-02-09 Parker Knight , Rui Duan

On the base of Lie algebraic and differential geometry methods, a wide class of multidimensional nonlinear integrable systems is obtained, and the integration scheme for such equations is proposed.

High Energy Physics - Theory · Physics 2008-02-03 A. V. Razumov , M. V. Saveliev

We design and test a cone finding algorithm to robustly address nonlinear system analysis through differential positivity. The approach provides a numerical tool to study multi-stable systems, beyond Lyapunov analysis. The theory is…

Optimization and Control · Mathematics 2019-09-17 Dimitris Kousoulidis , Fulvio Forni

Nonlinear field theories can be used to study both standard physics questions, or to study questions such as the emergence of order and complexity. These theories are generally derived from the symmetries of a given problem and the…

Adaptation and Self-Organizing Systems · Physics 2007-09-14 Joel Thorarinson , Marcelo Gleiser

Spin network systems can be used to achieve quantum state transfer with high fidelity and to generate entanglement. A new approach to design spin-chain-based spin network systems, for shortrange quantum information processing and…

Quantum Physics · Physics 2024-01-01 Abdulsalam H. Alsulami , Irene D'Amico , Marta P. Estarellas , Timothy P. Spiller

We demonstrate how quantum field theory problems can be embedded on quantum annealers. The general method we use is a discretisation of the field theory problem into a general Ising model, with the continuous field values being encoded into…

High Energy Physics - Phenomenology · Physics 2021-01-20 Steven Abel , Nicholas Chancellor , Michael Spannowsky

In this paper we study the possibility of constructing two-field models from one-field models. The idea is to start with a given one-field model and use the deformation procedure to generate another one-field model, and then couple the two…

High Energy Physics - Theory · Physics 2013-05-21 D. Bazeia , L. Losano , J. R. L. Santos

Qualitative possibilistic networks, also known as min-based possibilistic networks, are important tools for handling uncertain information in the possibility theory frame- work. Despite their importance, only the junction tree adaptation…

Artificial Intelligence · Computer Science 2012-03-19 Raouia Ayachi , Nahla Ben Amor , Salem Benferhat , Rolf Haenni

The past decade has witnessed a spectacular development of machine-learned interatomic potentials (MLIPs), to the extent that they are already the approach of choice for most atomistic simulation studies not requiring an explicit treatment…

Materials Science · Physics 2025-11-24 Iñigo Robredo-Magro , Binayak Mukherjee , Hugo Aramberri , Jorge Íñiguez-González

As more practical and scalable quantum computers emerge, much attention has been focused on realizing quantum supremacy in machine learning. Existing quantum ML methods either (1) embed a classical model into a target Hamiltonian to enable…

Quantum Physics · Physics 2022-10-05 Zhihao Zhang , Zhuoming Chen , Heyang Huang , Zhihao Jia

A framework for identifying nonlinear port-Hamiltonian systems using input-state-output data is introduced. The framework utilizes neural networks' universal approximation capacity to effectively represent complex dynamics in a structured…

Systems and Control · Electrical Eng. & Systems 2025-02-18 Karim Cherifi , Achraf El Messaoudi , Hannes Gernandt , Marco Roschkowski