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https://www.sciencedirect.com › journal › computer-methods-in-applied-…
Read the latest articles of Computer Methods in Applied Mechanics and Engineering at ScienceDirect Elsevier s leading platform of peer reviewed scholarly literature

https://www.sciencedirect.com › journal › computer-methods-in-applied-…
This title is given in special recognition of T J R Hughes distinguished service as Editor in Chief of CMAME for more than 40 years of guiding the journal tirelessly

https://www.sciencedirect.com › special-issue
Within the covers of Computer Methods in Applied Mechanics and Engineering have appeared in the fifty years of existence some of the most important works on record in

https://www.sciencedirect.com › journal › mechanical-systems-and-sign…
Mechanical Systems and Signal Processing MSSP is an interdisciplinary journal in Mechanical Aerospace and Civil Engineering aiming at reporting scientific advancements of the highest

https://www.sciencedirect.com › journal › engineering-applications-of-arti…
A journal of IFAC The Artificial Intelligence AI is playing a major role in the fourth industrial revolution and we are seeing a lot of evolution in various machine learning methodologies AI

https://www.sciencedirect.com › journal › computers-and-mathematics-w…
Computers Mathematics with Applications provides a medium of exchange for those engaged in fields contributing to building successful simulations for science and engineering using Partial

https://www.sciencedirect.com › journal › international-journal-of-solids-a…
The International Journal of Solids and Structures has as its objective the publication and dissemination of original research in Mechanics of Solids and Structures as a field of Applied

https://www.sciencedirect.com › journal › journal-of-the-mechanics-and-…
The Journal was founded in 1952 by Rodney Hill who was its Editor in Chief until 1968 It continues to enjoy a pre eminent position in the field of solid mechanics The Journal aims to

https://www.sciencedirect.com › science › article › pii
We present the application of a class of deep learning known as Physics Informed Neural Networks PINN to inversion and surrogate modeling in solid
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