Results 41 to 50 of about 18,903 (124)
Relational Bundle Geometric Formulation of Non‐Relativistic Quantum Mechanics
Fortschritte der Physik, Volume 73, Issue 12, December 2025.Abstract A bundle geometric formulation of non‐relativistic many‐particles Quantum Mechanics is presented. A wave function is seen to be a C$\mathbb {C}$‐valued cocyclic tensorial 0‐form on configuration space‐time seen as a principal bundle, while the Schrödinger equation flows from its covariant derivative, with the action functional supplying a ...
J. T. François, L. Ravera
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A Physics‐Informed Learning Framework to Solve the Infinite‐Horizon Optimal Control Problem
International Journal of Robust and Nonlinear Control, Volume 35, Issue 16, Page 6932-6944, 10 November 2025.ABSTRACT We propose a physics‐informed neural networks (PINNs) framework to solve the infinite‐horizon optimal control problem of nonlinear systems. In particular, since PINNs are generally able to solve a class of partial differential equations (PDEs), they can be employed to learn the value function of the infinite‐horizon optimal control problem via
Filippos Fotiadis +1 more
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Making vertebrate fossil radiocarbon dates more useful for global scientific research
Journal of Quaternary Science, Volume 40, Issue 8, Page 1309-1335, November 2025.ABSTRACT Radiocarbon dating of bones is essential for reconstructing timelines of species' occurrences, domestication, extinction, migrations, and interactions with Quaternary environments. Many studies compile these chronologies at continental to global scales by aggregating radiocarbon dates from various sources, often balancing data quantity and ...
Salvador Herrando‐Pérez +1 more
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Hamilton–Jacobi–Bellman Equations in Stochastic Geometric Mechanics
The 41st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 2022 Qiao Huang, Jean-Claude Zambrini
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A Level Set Topology Optimization Theory Based on Hamilton's Principle
International Journal for Numerical Methods in Engineering, Volume 126, Issue 17, 15 September 2025.ABSTRACT In this article, we propose a unified variational framework for deriving the evolution equation of the level set function in topology optimization, departing from conventional Hamilton–Jacobi‐based formulations. The key idea is the introduction of an auxiliary domain, geometrically identical to the physical design domain, occupied by ...
Jan Oellerich, Takayuki Yamada
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Discerned and Non-Discerned Particles in Classical Mechanics and Quantum Mechanics Interpretation
, 2010 We introduce into classical mechanics the concept of non-discerned particles for particles that are identical, non-interacting and prepared in the same way.
Gondran, Alexandre, Gondran, Michel
core
Astronomische Nachrichten, Volume 346, Issue 7-8, September 2025.ABSTRACT We investigate the effects of a minimal measurable length on neutron stars, within the quantum hadrodynamics (QHD‐I) model modified by the Generalized Uncertainty Principle (GUP). Working in a deformed Poisson algebra framework, we incorporate GUP effects via a time‐invariant transformation of the phase space volume, effectively reducing the ...
João Gabriel Galli Gimenez +2 more
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Fortschritte der Physik, Volume 73, Issue 8, August 2025.Abstract In this study, the properties, equilibrium, and stability of compact objects within the framework of teleparallel gravity with the generalized MIT bag model are investigated. By incorporating the modified field equations, the influence of the generalized bag constant on the structure and physical characteristics of quark stars and neutron ...
Sayantan Ghosh +2 more
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The de Broglie-Bohm weak interpretation
, 2017 We define the de Broglie-Bohm (dBB) weak interpretation as the dBB interpretation restricted to particles in unbound states whose wave function is defined in the three-dimensional physical space, and the dBB strong interpretation as the usual dBB ...
Gondran, Alexandre, Gondran, Michel
core
Reinforcement learning for optimal control of stochastic nonlinear systems
AIChE Journal, Volume 71, Issue 7, July 2025.Abstract A reinforcement learning (RL) approach is developed in this work for nonlinear systems under stochastic uncertainty. A stochastic control Lyapunov function (SCLF) candidate is first constructed using neural networks (NNs) as an approximator to the value function, and then a control policy designed using this SCLF is developed to ensure the ...
Xinji Zhu, Yujia Wang, Zhe Wu
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