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A bird’s-eye-view of the approach offered by the computational physics to the solution of the problems of physics is given with emphasis on the key issue of reliability. Some meanings of the reliability are illustrated by specific examples.

The universe we observe is homogeneous on super-horizon scales, leading to the “cosmic homogeneity problem”. Inflation alleviates this problem but cannot solve it within the realm of conservative extrapolations of classical physics. A probabilistic solution of the problem is possible but is subject to interpretational difficulties. A genuine deterministic solution of the homogeneity problem req...

Artists and scientists require tools to construct physics-based animal models. However, animating these models requires motion data for realistic movement. Motion data may either be measured from real-life animals-in-motion or generated using an optimisation approach. We propose a solution for retargeting gait data from one animal to another. The retargeted gait cycles are generated using a Gra...

A practical parameter extraction method is presented for the Fourier-based-solution physics-based IGBT model. In the extraction procedure, only one simple clamped inductive load test is needed for the extraction of the eleven and thirteen parameters required for the NPT and PT IGBT models, respectively. Validation with experimental results from various structure IGBTs demonstrates the accuracy ...

The theory of derivatives and integrals of fractional in fractional calculus have found enormousapplications in mathematics, physics and engineering so for that reason we need an efficient and accurate computational method for the solution of fractional differential equations. This paper presents a numerical method for solving a class of linear and nonlinear multi-order fractional differential ...

The universe we observe is homogeneous on super-horizon scales, leading to the “cosmic homogeneity problem”. Inflation alleviates this problem but cannot solve it within the realm of conservative extrapolations of classical physics. A probabilistic solution of the problem is possible but is subject to interpretational difficulties. A genuine deterministic solution of the homogeneity problem req...

We propose a stochastic projection-based gradient free physics-informed neural network. The proposed approach, referred to as the projection based physics informed network (SP-PINN), blends upscaled theory with recently This results in framework that is robust and can solve problems involving complex solution domain discontinuities. SP-PINN gradient-free approach which addresses computational b...

Physics-informed convolutional recurrent network (PhyCRNet) can solve partial differential equations without labeled data by encoding physics constraints into the loss function. However, finite-difference filter makes solution of 2D incompressible flow challenging. Hence, this paper proposes a Fourier filter-based physics-informed convolution (named PhyCRNet), which replaces in PhyCRNet with to...