Results 161 to 170 of about 543 (203)

Exploration of the soliton solutions of the (n+1) dimensional generalized Kadomstev Petviashvili equation using an innovative approach. [PDF]

Sci Rep
Kopçasız B, Sağlam FNK, Bulut H, Radwan T.   +3 more
europepmc   +1 more source

Invariant Boltzmann-Shannon Entropy for Black-Holes: A Manifestly-Covariant Canonical Quantum-Gravity Approach. [PDF]

Entropy (Basel)
Cremaschini C, Radhakrishnan R, Cleaver G.   +2 more
europepmc   +1 more source

Analyzing the neural wave structures in the field of neuroscience. [PDF]

Sci Rep
Younas U, Muhammad J, Almutairi DK, Khan A, Abdeljawad T.   +4 more
europepmc   +1 more source

Applications of the Mittag-Leffler law to linear kinetic models & diffusion equations. [PDF]

Sci Rep
Monyayi VT, Doungmo Goufo EF, Tchangou Toudjeu I.   +2 more
europepmc   +1 more source

Laplacian Eigenfunction-Based Neural Operator for Learning Nonlinear Reaction-Diffusion Dynamics. [PDF]

J Comput Phys
Wang J, Hao W.
europepmc   +1 more source

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BINet: Learning to Solve Partial Differential Equations with Boundary Integral Networks

arXiv.org, 2021
We propose a method combining boundary integral equations and neural networks (BINet) to solve partial differential equations (PDEs) in both bounded and unbounded domains.
Guochang Lin, Pipi Hu, Fukai Chen, Xiang Chen, Junqing Chen, Jun Wang, Zuo-Qiang Shi   +6 more
semanticscholar   +1 more source

Neural Evolutionary Kernel Method: A Knowledge-Guided Framework for Solving Evolutionary PDEs

arXiv.org
Numerical solution of partial differential equations (PDEs) plays a vital role in various fields of science and engineering. In recent years, deep neural networks (DNNs) have emerged as a powerful tool for solving PDEs, leveraging their approximation ...
Shuo Ling, Wenjun Ying, Zhen Zhang
semanticscholar   +1 more source

Rigorous Analytical Solution for the Linearized Whitham–Broer–Kaup System on the Half‐Line

Studies in applied mathematics (Cambridge)
The higher‐order Whitham–Broer–Kaup (WBK) system is a pair of coupled nonlinear evolution partial differential equations (PDEs) describing the interaction between long and short dispersive waves in shallow water.
A. Chatziafratis, G. Karali, C. Synolakis   +2 more
semanticscholar   +1 more source

Neural Operators with Localized Integral and Differential Kernels

International Conference on Machine Learning
Neural operators learn mappings between function spaces, which is practical for learning solution operators of PDEs and other scientific modeling applications.
Miguel Liu-Schiaffini, Julius Berner, B. Bonev, Thorsten Kurth, K. Azizzadenesheli, Anima Anandkumar   +5 more
semanticscholar   +1 more source