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Discovering valid and meaningful mathematical equations from observed data plays a crucial role in scientific discovery. While this task, symbolic regression, remains challenging due to the vast search space and the trade-off between…

Machine Learning · Computer Science 2025-09-17 Xiaoxu Han , Chengzhen Ning , Jinghui Zhong , Fubiao Yang , Yu Wang , Xin Mu

Neural networks have the ability to serve as universal function approximators, but they are not interpretable and don't generalize well outside of their training region. Both of these issues are problematic when trying to apply standard…

Machine Learning · Computer Science 2023-08-21 Colby Fronk , Linda Petzold

Deep neural networks (DNNs) have achieved exceptional performance across various fields by learning complex, nonlinear mappings from large-scale datasets. However, they face challenges such as high memory requirements and computational…

Machine Learning · Computer Science 2025-04-21 Callen MacPhee , Yiming Zhou , Bahram Jalali

In this paper we present a new method for deriving It\^{o} stochastic delay differential equations (SDDEs) from delayed chemical master equations (DCMEs). Considering alternative formulations of SDDEs that can be derived from the same DCME,…

Chaotic Dynamics · Physics 2023-05-09 F. Fatehi , Y. N. Kyrychko , K. B. Blyuss

We present quantum algorithms for electromagnetic fields governed by Maxwell's equations. The algorithms are based on the Schr\"odingersation approach, which transforms any linear PDEs and ODEs with non-unitary dynamics into a system…

Quantum Physics · Physics 2023-08-17 Shi Jin , Nana Liu , Chuwen Ma

Quantum simulation is known to be capable of simulating certain dynamical systems in continuous time -- Schrodinger's equations being the most direct and well-known -- more efficiently than classical simulation. Any linear dynamical system…

Quantum Physics · Physics 2025-04-22 Shi Jin , Nana Liu

In the present paper, a discrete differential calculus is introduced and used to describe dynamical systems over arbitrary graphs. The discretization of space and time allows the derivation of Heisenberg-like uncertainty inequalities and of…

Statistical Mechanics · Physics 2009-11-10 Demian Battaglia , Mario Rasetti

This paper describes a package for calculations of expressions with Dirac matrixes. Advantages to existing similar packages are described. MatrixExp package is intended for simplification of complex expressions involving $\gamma$-matrixes,…

High Energy Physics - Phenomenology · Physics 2009-11-11 V. A. Poghosyan

We present a software package DiracQ, for use in quantum many-body Physics. It is designed for helping with typical algebraic manipulations that arise in quantum Condensed Matter Physics and Nuclear Physics problems, and also in some…

Strongly Correlated Electrons · Physics 2026-01-16 John G. Wright , B. Sriram Shastry

We develop the discrete set of Dyson-Schwinger equations for scalar fields and solve them for some cases. We show that their solutions are Gaussian in the continuum limit as expected from the theorems of Aizenman and of Aizenman and…

Mathematical Physics · Physics 2026-05-15 Marco Frasca

We apply the Simple Equations Method (SEsM) for obtaining exact solutions of nonlinear differential equations. We discuss several examples with goal to illustrate the results from the use of derivatives of composite functions in the…

Exactly Solvable and Integrable Systems · Physics 2024-11-13 Zlatinka I. Dimitrova

We present a system for generating parsers based directly on the metaphor of parsing as deduction. Parsing algorithms can be represented directly as deduction systems, and a single deduction engine can interpret such deduction systems so as…

cmp-lg · Computer Science 2008-02-03 Stuart M. Shieber , Yves Schabes , Fernando C. N. Pereira

The theory of Poisson-$\sigma$-models employs the mathematical notion of Poisson manifolds to formulate and analyze a large class of topological and almost topological two dimensional field theories. As special examples this class of field…

High Energy Physics - Theory · Physics 2015-06-26 Peter Schaller , Thomas Strobl

We show the power of Bruno Buchberger's seminal Groebner Basis algorithm, interfaced, seamlessly, with what we call symbolic dynamical programming, to automatically generate algebraic equations satisfied by the generating functions…

Combinatorics · Mathematics 2023-05-17 AJ Bu , Doron Zeilberger

The Dyson series is an infinite sum of multi-dimensional time-ordered integrals, which serves as a formal representation of the quantum time-evolution operator in the interaction-picture. Using the mathematical tool of divided differences,…

Quantum Physics · Physics 2024-06-18 Amir Kalev , Itay Hen

In this paper we study the representation of partial differential equations (PDEs) as abstract differential-algebraic equations (DAEs) with dissipative Hamiltonian structure (adHDAEs). We show that these systems not only arise when there…

Functional Analysis · Mathematics 2024-05-20 Volker Mehrmann , Hans Zwart

The time-dependent Schrodinger equation (TDSE) is usually treated in real space in the textbook. However, it makes the numerical simulations of strong-field processes difficult due to the wide dispersion and fast oscillation of the electron…

Atomic Physics · Physics 2020-01-20 Yan Xu , Xue-Bin Bian

We develop an algorithm for the computation of general Fourier integral operators associated with canonical graphs. The algorithm is based on dyadic parabolic decomposition using wave packets and enables the discrete approximate evaluation…

Numerical Analysis · Mathematics 2015-05-27 Maarten V. de Hoop , Gunther Uhlmann , Andras Vasy , Herwig Wendt

We suggest a method for integrating sub-families of a family of nonlinear {\sc Schr\"odinger} equations proposed by {\sc H.-D.~Doebner} and {\sc G.A.~Goldin} in the 1+1 dimensional case which have exceptional {\sc Lie} symmetries. Since the…

solv-int · Physics 2008-11-26 P. Nattermann , R. Zhdanov

The quest for analytical solutions to differential equations has traditionally been constrained by the need for extensive mathematical expertise. Machine learning methods like genetic algorithms have shown promise in this domain, but are…

Machine Learning · Computer Science 2025-07-22 Shu Wei , Yanjie Li , Lina Yu , Weijun Li , Min Wu , Linjun Sun , Jingyi Liu , Hong Qin , Yusong Deng , Jufeng Han , Yan Pang
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