Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Meidani, Kazem"'
Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines. However, discovering such insightful equations from data presents significant challenges due to the necessity of na
Externí odkaz:
http://arxiv.org/abs/2404.18400
In an era where symbolic mathematical equations are indispensable for modeling complex natural phenomena, scientific inquiry often involves collecting observations and translating them into mathematical expressions. Recently, deep learning has emerge
Externí odkaz:
http://arxiv.org/abs/2310.02227
Symbolic regression (SR) is a challenging task in machine learning that involves finding a mathematical expression for a function based on its values. Recent advancements in SR have demonstrated the effectiveness of pre-trained transformer-based mode
Externí odkaz:
http://arxiv.org/abs/2303.06833
Data-driven learning of partial differential equations' solution operators has recently emerged as a promising paradigm for approximating the underlying solutions. The solution operators are usually parameterized by deep learning models that are buil
Externí odkaz:
http://arxiv.org/abs/2205.13671
Autor:
Akbari, Parand, Ogoke, Francis, Kao, Ning-Yu, Meidani, Kazem, Yeh, Chun-Yu, Lee, William, Farimani, Amir Barati
Characterizing meltpool shape and geometry is essential in metal Additive Manufacturing (MAM) to control the printing process and avoid defects. Predicting meltpool flaws based on process parameters and powder material is difficult due to the complex
Externí odkaz:
http://arxiv.org/abs/2201.11662
Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter. Since the resolution of MD is atomic-scale, achieving long time-scale simulations with femtosecond integration is very expensive. In each MD
Externí odkaz:
http://arxiv.org/abs/2112.03383
Many scientific and engineering processes produce spatially unstructured data. However, most data-driven models require a feature matrix that enforces both a set number and order of features for each sample. They thus cannot be easily constructed for
Externí odkaz:
http://arxiv.org/abs/2012.02232
Autor:
Meidani, Kazem, Farimani, Amir Barati
Many scientific phenomena are modeled by Partial Differential Equations (PDEs). The development of data gathering tools along with the advances in machine learning (ML) techniques have raised opportunities for data-driven identification of governing
Externí odkaz:
http://arxiv.org/abs/2010.10626
Autor:
Meidani, Kazem, Barati Farimani, Amir
Publikováno v:
In Expert Systems With Applications 1 June 2023 219
Publikováno v:
In Applied Soft Computing Journal October 2022 128