Difference schemes of high order accuracy for mathematical physics problems in arbitrary domains
In the present paper the difference schemes of high order accuracy for two‐dimensional equations of mathematical physics in an arbitrary domain are constructed. The computational domain is covered by a uniform rectangular grid.
P. P. Matus, A. N. Zyl
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HOW PHYSICISTS WASTE SUPERCOMPUTERS TIME IN ACADEMIC COMPUTER CENTER CYFRONET-KRAKOW
In this paper computer facilities for scientific research groups in Cracow arc presented. Some problems solved by using supercomputers in Academic Computer Center Cyfronet-Krakow are discussed. The main flowcharts of computer algorithms and programs are
KRZYSZTOF MALARZ +2 more
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AI-driven predictive modelling of residual stress of HVOF thermal sprayed carbon-based composite coatings using physics-informed neural networks. [PDF]
Tyagi A, Dadhich A, Sirohi S, Gupta AK.
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Co-design of programmable material systems. [PDF]
Lee DD +7 more
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Improving Reliability of Machine Learning Interatomic Potentials with Physics-Informed Pretraining. [PDF]
Zheng Q, Fung V.
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A Physics-Informed Benchmarking Framework for Machine Learning and Tree-Based Ensembles in IIoT-Enabled Predictive Maintenance. [PDF]
Su YK, Tseng CJ.
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Phys-Mamba: Physics-informed selective state-space fusion network for high-fidelity underwater image restoration. [PDF]
Li H +7 more
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Physics-informed neural network framework for predicting drilling-induced delamination in GFRP composites. [PDF]
Arunadevi M, M S S, B R N M, M C G.
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Artificial Intelligence in Particle Therapy Treatment Planning: Current Advances and Future Directions. [PDF]
Chow JCL, Giap H.
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Physics-Informed Neural Networks Meet Multimodal Large Language Models: Biomechanical Simulation in Aortic Aneurysm. [PDF]
Zhu S +7 more
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