Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Khemraj Shukla"'
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-14 (2024)
Abstract Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of
Externí odkaz:
https://doaj.org/article/6da11ea4e1c24b6d83638be23b0a7844
Publikováno v:
PLoS Computational Biology, Vol 20, Iss 3 (2024)
Externí odkaz:
https://doaj.org/article/c2132141ef5d432894885e504a5473eb
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-13 (2022)
Abstract Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and generalizable surrogate models are needed to alleviate the cost of
Externí odkaz:
https://doaj.org/article/48b877fa6c20433d84313024f47ceeb5
Publikováno v:
Data-Centric Engineering, Vol 3 (2022)
Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the o
Externí odkaz:
https://doaj.org/article/d710648549694e99ae6855cb0d60d54f
Publikováno v:
IEEE Signal Processing Magazine. 39:68-77
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e1254a34f0eca8dac75284c37c81730f
https://doi.org/10.1109/msp.2021.3118904
https://doi.org/10.1109/msp.2021.3118904
We compare high-order methods including spectral difference (SD), flux reconstruction (FR), the entropy-stable discontinuous Galerkin spectral element method (ES-DGSEM), modal discontinuous Galerkin methods, and WENO to select the best candidate to s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25b1247a8c523c58d231f7efd781756e
http://arxiv.org/abs/2211.12635
http://arxiv.org/abs/2211.12635
Publikováno v:
Journal of Geophysical Research: Solid Earth. 127
We propose a new approach to the solution of the wave propagation and full waveform inversions (FWIs) based on a recent advance in deep learning called Physics-Informed Neural Networks (PINNs). In this study, we present an algorithm for PINNs applied
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86e36f712178d2103d8ea8a7466c92be
https://doi.org/10.1029/2021jb023120
https://doi.org/10.1029/2021jb023120
Autor:
Khemraj Shukla, Iftekhar Alam
Publikováno v:
Geophysical Prospecting. 68:2078-2093
Seismic modelling of the shallow subsurface (within the first few metres) is often challenging when the data are dominated by ground‐roll and devoid of reflection. We showed that, even when transmission is the only available phase for analysis, fin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28604f5e48c63b57cbbc59007505edbb
https://doi.org/10.1111/1365-2478.12987
https://doi.org/10.1111/1365-2478.12987
Publikováno v:
Computers & Geosciences. 126:31-40
We present an efficient and accurate modeling approach for wave propagation in anelastic media, based on a fractional spatial differential operator. The problem is solved with the Fourier pseudo-spectral method in the spatial domain and the REM (rapi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::db83e4f043c8aec9890e023e83333f14
https://doi.org/10.1016/j.cageo.2019.01.022
https://doi.org/10.1016/j.cageo.2019.01.022
Autor:
Khemraj Shukla, José M. Carcione, Jan S. Hesthaven, Priyank Jaiswal, Ruichao Ye, Josep de la Puente
Publikováno v:
Computational Geosciences. 23:595-615
We use the nodal discontinuous Galerkin method with a Lax-Friedrich flux to model the wave propagation in transversely isotropic and poroelastic media. The effect of dissipation due to global fluid flow causes a stiff relaxation term, which is incorp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0124a9e13bfc23407f7e367c7f01af16
https://doi.org/10.1007/s10596-019-9809-1
https://doi.org/10.1007/s10596-019-9809-1