Results 81 to 90 of about 9,582 (250)
Physics informed neural network (PINN) demonstrates powerful capabilities in solving forward and inverse problems of nonlinear partial differential equations (NLPDEs) through combining data-driven and physical constraints. In this paper, two PINN methods
Jiajun Chen +3 more
doaj +1 more source
Application of Symbolic Computation in Nonlinear Differential-Difference Equations
A method is proposed to construct closed-form solutions of nonlinear differential-difference equations. For the variety of nonlinearities, this method only deals with such equations which are written in polynomials in function and its derivative.
Fuding Xie, Zhen Wang, Min Ji
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In this paper, by using the improved tanhmethod, the hybridlattice system an d Ablowitzladiklattice system are reduced to nonlinear algebraic equations, and then the new exact solutions for these equations, which include exact soliton wave solutions and periodic solutions, are obtained through solving these nonlinear algebraic equations.
null Zhu Jia-Min +2 more
openaire +1 more source
In‐Memory Continuous‐Time SAT Solver Based on Bidirectional 11‐T SRAM Macro
This article reported a continuous‐time (CT) Boolean satisfiability (SAT) problem solver using bidirectional 11T‐SRAM macro. The proposed system operates asynchronously using capacitor‐based gradient integration and maximizes the parallelism for SAT solving by in‐memory computing (IMC).
Dongseok Kwon +3 more
wiley +1 more source
Convergence and Empirical Performance of Tanh-Based Adaptive Particle Swarm Optimization
Particle Swarm Optimization (PSO) is a widely used population-based optimization method but faces challenges in premature convergence, leading to suboptimal solutions.
Joko Riyono +5 more
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The phase discontinuity problem—where the cyclic nature of phase angles causes catastrophic errors near the ±π boundary—is a fundamental obstacle in learning‐based reconfigurable intelligent surface (RIS) optimization. A phase‐aware hybrid CNN–LSTM framework resolves this by decomposing phase predictions into sine–cosine components, mapping circular ...
Seda Savaşçı Şen +3 more
wiley +1 more source
Neuro‐evolution can boost machine‐learning optimization of chiral metasurfaces. By integrating the NEAT algorithm into a deep‐learning framework, we enable the efficient design of visible‐spectrum chiroptical responses. NEAT autonomously evolves neural‐network architectures and weights, reducing manual tuning.
Davide Filippozzi, Arash Rahimi‐Iman
wiley +1 more source
Radiative Hybrid Nanofluid Flow Over a Porous Riga Surface: A Fuzzy–ANN Modeling Approach
ABSTRACT This study proposes a fuzzy–ANN model to investigate the nonlinear thermal transport in a tangent hyperbolic (Tanh) hybrid nanofluid flow past a porous Riga surface, considering the effects of Rosseland diffusion, chemical reactions, and internal volumetric heating.
Azad Hussain, Rabia Zetoon, Reeha Iqbal
wiley +1 more source
In this paper, the improved modified extended Tanh function technique is used to find analytical solutions to the cubic-quartic Lakshmanan-Porsezian-Daniel model which describes the pulse propagation in optical fiber. Using the traveling wave theory, the
Hisham H. Hussein +2 more
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Three Different Methods for New Soliton Solutions of the Generalized NLS Equation
Three different methods are applied to construct new types of solutions of nonlinear evolution equations. First, the Csch method is used to carry out the solutions; then the Extended Tanh-Coth method and the modified simple equation method are used to ...
Anwar Ja’afar Mohamad Jawad
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