Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Mojgani, Rambod"'
Global climate models (GCMs) are the main tools for understanding and predicting climate change. However, due to limited numerical resolutions, these models suffer from major structural uncertainties; e.g., they cannot resolve critical processes such
Externí odkaz:
http://arxiv.org/abs/2312.00907
Earth system models suffer from various structural and parametric errors in their representation of nonlinear, multi-scale processes, leading to uncertainties in their long-term projections. The effects of many of these errors (particularly those due
Externí odkaz:
http://arxiv.org/abs/2309.13211
There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we apply a common equation-discovery technique with expansive libraries to l
Externí odkaz:
http://arxiv.org/abs/2306.05014
Physics-informed neural networks (PINNs) leverage neural-networks to find the solutions of partial differential equation (PDE)-constrained optimization problems with initial conditions and boundary conditions as soft constraints. These soft constrain
Externí odkaz:
http://arxiv.org/abs/2205.02902
Publikováno v:
Chaos 32, 061105 (2022)
Models of many engineering and natural systems are imperfect. The discrepancy between the mathematical representations of a true physical system and its imperfect model is called the model error. These model errors can lead to substantial differences
Externí odkaz:
http://arxiv.org/abs/2110.00546
Autor:
Mojgani, Rambod1 (AUTHOR) rmojgani@uchicago.edu, Chattopadhyay, Ashesh1,2 (AUTHOR), Hassanzadeh, Pedram1,3 (AUTHOR)
Publikováno v:
Journal of Advances in Modeling Earth Systems. Mar2024, Vol. 16 Issue 3, p1-22. 22p.
Autor:
Mojgani, Rambod, Balajewicz, Maciej
We design a physics-aware auto-encoder to specifically reduce the dimensionality of solutions arising from convection-dominated nonlinear physical systems. Although existing nonlinear manifold learning methods seem to be compelling tools to reduce th
Externí odkaz:
http://arxiv.org/abs/2006.15655
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 February 2023 404
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Autor:
Mojgani, Rambod, Balajewicz, Maciej
Foundations of a new projection-based model reduction approach for convection dominated nonlinear fluid flows are summarized. In this method the evolution of the flow is approximated in the Lagrangian frame of reference. Global basis functions are us
Externí odkaz:
http://arxiv.org/abs/1701.04343