Zobrazeno 1 - 10
of 1 583
pro vyhledávání: '"Santos, Javier A."'
We investigate the use of the Senseiver, a transformer neural network designed for sparse sensing applications, to estimate full-field surface height measurements of tsunami waves from sparse observations. The model is trained on a large ensemble of
Externí odkaz:
http://arxiv.org/abs/2411.12948
Autor:
Marcato, Agnese, Santos, Javier E., Pachalieva, Aleksandra, Gao, Kai, Hill, Ryley, Rougier, Esteban, Kang, Qinjun, Hyman, Jeffrey, Hunter, Abigail, Chua, Janel, Lawrence, Earl, Viswanathan, Hari, O'Malley, Daniel
Understanding material failure is critical for designing stronger and lighter structures by identifying weaknesses that could be mitigated. Existing full-physics numerical simulation techniques involve trade-offs between speed, accuracy, and the abil
Externí odkaz:
http://arxiv.org/abs/2411.08354
This paper presents a novel benchmark where the large language model (LLM) must write code that computes integer sequences from the Online Encyclopedia of Integer Sequences (OEIS), a widely-used resource for mathematical sequences. The benchmark is d
Externí odkaz:
http://arxiv.org/abs/2411.04372
Autor:
Bhattarai, Manish, Santos, Javier E., Jones, Shawn, Biswas, Ayan, Alexandrov, Boian, O'Malley, Daniel
The advent of large language models (LLMs) has significantly advanced the field of code translation, enabling automated translation between programming languages. However, these models often struggle with complex translation tasks due to inadequate c
Externí odkaz:
http://arxiv.org/abs/2407.19619
Autor:
Chung, Jaehong, Marcato, Agnese, Guiltinan, Eric J., Mukerji, Tapan, Viswanathan, Hari, Lin, Yen Ting, Santos, Javier E.
This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics-based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of in
Externí odkaz:
http://arxiv.org/abs/2406.19333
Recreating complex, high-dimensional global fields from limited data points is a grand challenge across various scientific and industrial domains. Given the prohibitive costs of specialized sensors and the frequent inaccessibility of certain regions
Externí odkaz:
http://arxiv.org/abs/2312.09176
Autor:
Chung, Jaehong, Marcato, Agnese, Guiltinan, Eric J., Mukerji, Tapan, Lin, Yen Ting, Santos, Javier E.
Pore-scale simulations accurately describe transport properties of fluids in the subsurface. These simulations enhance our understanding of applications such as assessing hydrogen storage efficiency and forecasting CO$_2$ sequestration processes in u
Externí odkaz:
http://arxiv.org/abs/2312.04375
Autor:
Santos, Javier E., Marcato, Agnese, Kang, Qinjun, Mehana, Mohamed, O'Malley, Daniel, Viswanathan, Hari, Lubbers, Nicholas
Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics - in particular, confinement effects which vary across
Externí odkaz:
http://arxiv.org/abs/2310.14298
In this paper, we introduce Pysimfrac, a open-source python library for generating 3-D synthetic fracture realizations, integrating with fluid simulators, and performing analysis. Pysimfrac allows the user to specify one of three fracture generation
Externí odkaz:
http://arxiv.org/abs/2309.13849
Autor:
Sweeney, Matthew R., Hyman, Jeffrey D., O'Malley, Daniel, Santos, Javier E., Carey, J. William, Stauffer, Philip H., Viswanathan, Hari S.
We model flow and transport in three-dimensional fracture networks with varying degrees of fracture-to-fracture aperture/permeability heterogeneity and network density to show how changes in these properties can cause the emergence of anomalous flow
Externí odkaz:
http://arxiv.org/abs/2306.00773