Zobrazeno 1 - 10
of 209
pro vyhledávání: '"Nabian, Mohammad A."'
Publikováno v:
Sensors and Materials 2024
Metal Sintering is a necessary step for Metal Injection Molded parts and binder jet such as HP's metal 3D printer. The metal sintering process introduces large deformation varying from 25 to 50% depending on the green part porosity. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2404.11753
Autor:
Mardani, Morteza, Brenowitz, Noah, Cohen, Yair, Pathak, Jaideep, Chen, Chieh-Yu, Liu, Cheng-Chin, Vahdat, Arash, Nabian, Mohammad Amin, Ge, Tao, Subramaniam, Akshay, Kashinath, Karthik, Kautz, Jan, Pritchard, Mike
The state of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such glob
Externí odkaz:
http://arxiv.org/abs/2309.15214
Autor:
Li, Zongyi, Kovachki, Nikola Borislavov, Choy, Chris, Li, Boyi, Kossaifi, Jean, Otta, Shourya Prakash, Nabian, Mohammad Amin, Stadler, Maximilian, Hundt, Christian, Azizzadenesheli, Kamyar, Anandkumar, Anima
We propose the geometry-informed neural operator (GINO), a highly efficient approach to learning the solution operator of large-scale partial differential equations with varying geometries. GINO uses a signed distance function and point-cloud represe
Externí odkaz:
http://arxiv.org/abs/2309.00583
Physics-Informed Neural Networks (PINNs) are a class of deep learning neural networks that learn the response of a physical system without any simulation data, and only by incorporating the governing partial differential equations (PDEs) in their los
Externí odkaz:
http://arxiv.org/abs/2210.14320
Autor:
Bharadwaja, B V S S, Nabian, Mohammad Amin, Sharma, Bharatkumar, Choudhry, Sanjay, Alankar, Alankar
In this work, a model based on the Physics - Informed Neural Networks (PINNs) for solving elastic deformation of heterogeneous solids and associated Uncertainty Quantification (UQ) is presented. For the present study, the PINNs framework - Modulus de
Externí odkaz:
http://arxiv.org/abs/2202.10423
Publikováno v:
In Medical Engineering and Physics July 2024 129
Robust topology optimization (RTO), as a class of topology optimization problems, identifies a design with the best average performance while reducing the response sensitivity to input uncertainties, e.g. load uncertainty. Solving RTO is computationa
Externí odkaz:
http://arxiv.org/abs/2107.10661
Autor:
Nabian, Mohammad Hossein, Zadegan, Shayan Abdollah, Mallet, Cindy, Neder, Yamile, Ilharreborde, Brice, Simon, Anne Laure, Presedo, Ana
Publikováno v:
In Gait & Posture May 2024 110:53-58
Physics-Informed Neural Networks (PINNs) are a class of deep neural networks that are trained, using automatic differentiation, to compute the response of systems governed by partial differential equations (PDEs). The training of PINNs is simulation-
Externí odkaz:
http://arxiv.org/abs/2104.12325
Autor:
Hennigh, Oliver, Narasimhan, Susheela, Nabian, Mohammad Amin, Subramaniam, Akshay, Tangsali, Kaustubh, Rietmann, Max, Ferrandis, Jose del Aguila, Byeon, Wonmin, Fang, Zhiwei, Choudhry, Sanjay
We present SimNet, an AI-driven multi-physics simulation framework, to accelerate simulations across a wide range of disciplines in science and engineering. Compared to traditional numerical solvers, SimNet addresses a wide range of use cases - coupl
Externí odkaz:
http://arxiv.org/abs/2012.07938