Results 41 to 50 of about 37,100 (232)
Are physiological oscillations physiological?
The Journal of Physiology, EarlyView., 2023 Abstract figure legend Mechanisms and functions of physiological oscillations. Abstract Despite widespread and striking examples of physiological oscillations, their functional role is often unclear. Even glycolysis, the paradigm example of oscillatory biochemistry, has seen questions about its oscillatory function.
Lingyun (Ivy) Xiong, Alan Garfinkel
wiley +1 more source
Entropy, 2023 Physics-informed neural networks (PINNs) are effective for solving partial differential equations (PDEs). This method of embedding partial differential equations and their initial boundary conditions into the loss functions of neural networks has ...
Kuo Sun, Xinlong Feng
doaj +1 more source
International Journal of Mathematics and Mathematical Sciences, 2014 Singular nonlinear initial-value problems (IVPs) in first-order and second-order partial differential equations (PDEs) arising in fluid mechanics are semianalytically solved. To achieve this, the modified decomposition method (MDM) is used in conjunction
Nemat Dalir
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A discrete linearizability test based on multiscale analysis [PDF]
, 2010 In this paper we consider the classification of dispersive linearizable partial difference equations defined on a quad-graph by the multiple scale reduction around their harmonic solution.
Agrotis M +22 more
core +2 more sources
International Transactions on Electrical Energy Systems, 2023 In this paper, the development and application of the radial basis function-finite difference (RBF-FD) method and the RBF-finite difference time domain (RBF-FDTD) method for solving electrical transient problems in power systems that are defined by the ...
Duc-Quang Vu +2 more
doaj +1 more source
A class of second-order geometric quasilinear hyperbolic PDEs and their application in imaging science [PDF]
, 2019 In this paper, we study damped second-order dynamics, which are quasilinear hyperbolic partial differential equations (PDEs). This is inspired by the recent development of second-order damping systems for accelerating energy decay of gradient flows.
Dong, Guozhi +2 more
core +3 more sources
Consensus ADMM for Inverse Problems Governed by Multiple PDE Models
, 2021 The Alternating Direction Method of Multipliers (ADMM) provides a natural way of solving inverse problems with multiple partial differential equations (PDE) forward models and nonsmooth regularization. ADMM allows splitting these large-scale inverse problems into smaller, simpler sub-problems, for which computationally efficient solvers are available ...
Lozenski, Luke, Villa, Umberto
openaire +2 more sources
Gradient Statistics-Based Multi-Objective Optimization in Physics-Informed Neural Networks
Sensors, 2023 Modeling and simulation of complex non-linear systems are essential in physics, engineering, and signal processing. Neural networks are widely regarded for such tasks due to their ability to learn complex representations from data.
Sai Karthikeya Vemuri, Joachim Denzler
doaj +1 more source
Semivariogram methods for modeling Whittle-Mat\'ern priors in Bayesian inverse problems
, 2020 We present a new technique, based on semivariogram methodology, for obtaining point estimates for use in prior modeling for solving Bayesian inverse problems.
Bardsley, Johnathan M. +2 more
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Axioms, 2023 We study three-dimensional potentials of the form V=U(xp+yp+zp), where U is an arbitrary function of C2-class, and p∈Z, which produces a preassigned two-parametric family of spatial regular orbits given in the solved form f(x,y,z) = c1, g(x,y,z) = c2 (c1,
Thomas Kotoulas
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