481 citations · 730 across the 13 of their papers we have counts for
13 papers
Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics
Hamidreza Eivazi, Henning Wessels
Physics-informed operator learning is an attractive candidate for surrogate modeling of microstructures, especially in multiscale finite-element simulations. Its practical use, how…
A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials
Dhananjeyan Jeyaraj, Hamidreza Eivazi, Jendrik-Alexander Tröger +3
The behavior of materials is influenced by a wide range of phenomena occurring across various time and length scales. To better understand the impact of microstructure on macroscop…
EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations
Hamidreza Eivazi, Jendrik-Alexander Tröger, Stefan Wittek +2
Multiscale problems are ubiquitous in physics. Numerical simulations of such problems by solving partial differential equations (PDEs) at high resolution are computationally too ex…
A Spatiotemporal Radar-Based Precipitation Model for Water Level Prediction and Flood Forecasting
Sakshi Dhankhar, Stefan Wittek, Hamidreza Eivazi +1
Study Region: Goslar and Göttingen, Lower Saxony, Germany. Study Focus: In July 2017, the cities of Goslar and Göttingen experienced severe flood events characterized by short warn…
DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis
Hamidreza Eivazi, André Hebenbrock, Raphael Ginster +6
Battery degradation remains a critical challenge in the pursuit of green technologies and sustainable energy solutions. Despite significant research efforts, predicting battery cap…
Enhancing Multiscale Simulations with Constitutive Relations-Aware Deep Operator Networks
Hamidreza Eivazi, Mahyar Alikhani, Jendrik-Alexander Tröger +3
Multiscale problems are widely observed across diverse domains in physics and engineering. Translating these problems into numerical simulations and solving them using numerical sc…
Nonlinear model reduction for operator learning
Hamidreza Eivazi, Stefan Wittek, Andreas Rausch
Operator learning provides methods to approximate mappings between infinite-dimensional function spaces. Deep operator networks (DeepONets) are a notable architecture in this field…
Predicting the temporal dynamics of turbulent channels through deep learning
Giuseppe Borrelli, Luca Guastoni, Hamidreza Eivazi +2
The success of recurrent neural networks (RNNs) has been demonstrated in many applications related to turbulence, including flow control, optimization, turbulent features reproduct…
Physics-informed deep-learning applications to experimental fluid mechanics
Hamidreza Eivazi, Yuning Wang, Ricardo Vinuesa
High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics,…
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
Hamidreza Eivazi, Soledad Le Clainche, Sergio Hoyas +1
We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of non-linear modes from high-fidelity turbulent-flow-field data useful f…
Physics-informed neural networks for solving Reynolds-averaged Navier$\unicode{x2013}$Stokes equations
Hamidreza Eivazi, Mojtaba Tahani, Philipp Schlatter +1
Physics-informed neural networks (PINNs) are successful machine-learning methods for the solution and identification of partial differential equations (PDEs). We employ PINNs for s…
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows
Hamidreza Eivazi, Hadi Veisi, Mohammad Hossein Naderi +1
Unsteady fluid systems are nonlinear high-dimensional dynamical systems that may exhibit multiple complex phenomena both in time and space. Reduced Order Modeling (ROM) of fluid fl…
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence
Hamidreza Eivazi, Luca Guastoni, Philipp Schlatter +2
The capabilities of recurrent neural networks and Koopman-based frameworks are assessed in the prediction of temporal dynamics of the low-order model of near-wall turbulence by Moe…