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Autor:
Toscano, Juan Diego, Oommen, Vivek, Varghese, Alan John, Zou, Zongren, Daryakenari, Nazanin Ahmadi, Wu, Chenxi, Karniadakis, George Em
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the pa
Externí odkaz:
http://arxiv.org/abs/2410.13228
We integrate neural operators with diffusion models to address the spectral limitations of neural operators in surrogate modeling of turbulent flows. While neural operators offer computational efficiency, they exhibit deficiencies in capturing high-f
Externí odkaz:
http://arxiv.org/abs/2409.08477
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential to gaining value from electronic health records (EHR) in the support of precision medicine. Despite technological advance
Externí odkaz:
http://arxiv.org/abs/2406.14757
Autor:
Shukla, Khemraj, Ratchford, Jasmine, Bravo, Luis, Oommen, Vivek, Plewacki, Nicholas, Ghoshal, Anindya, Karniadakis, George
Neural networks are universal approximators that traditionally have been used to learn a map between function inputs and outputs. However, recent research has demonstrated that deep neural networks can be used to approximate operators, learning funct
Externí odkaz:
http://arxiv.org/abs/2405.13234
Publikováno v:
J. Fluids Eng. 132 (2010) 011303
The study deals with the breakup behavior of swirling liquid sheets discharging from gas-centered swirl coaxial atomizers with attention focused toward the understanding of the role of central gas jet on the liquid sheet breakup. Cold flow experiment
Externí odkaz:
http://arxiv.org/abs/2405.08264
Human-wildlife conflict challenges conservation worldwide, which requires innovative management solutions. We developed a prototype Agent-Based Model (ABM) to simulate interactions between humans and solitary bull Asian elephants in the Periyar-Agast
Externí odkaz:
http://arxiv.org/abs/2404.09024
Autor:
Pattanaik, Nupur
Publikováno v:
Sociological Bulletin; July 2023, Vol. 72 Issue: 3 p354-357, 4p
Developing the proper representations for simulating high-speed flows with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numerical analysis. Herein, we employ neural operators to solve Riemann prob
Externí odkaz:
http://arxiv.org/abs/2401.08886
Direct numerical simulations (DNS) are accurate but computationally expensive for predicting materials evolution across timescales, due to the complexity of the underlying evolution equations, the nature of multiscale spatio-temporal interactions, an
Externí odkaz:
http://arxiv.org/abs/2312.05410