We investigated two methods for incorporation of prior physics-based knowledge from a reduced-order model (ROM) into NNs that predicted pressure losses across stenotic and healthy coronary segments. First, we trained NNs to predict the discrepancy between the ROM and 3DiNS pressure loss. Second, ...
Machine learningCrack interactionReduced-order modelsGraph theoryTypically, thousands of computationally expensive micro-scale simulations of brittle crack propagation are needed to upscale lower length scale phenomena to the macro-continuum scale. Running such a large number of crack propagation simulations ...
We propose a method to construct a reduced order model with machine learning for unsteady flows. The present machine-learned reduced order model (ML-ROM) is constructed by combining a convolutional neural network autoencoder (CNN-AE) and a long short-term memory (LSTM), which are trained in ...
How do Reduced Order Modeling, Machine Learning, and Hybrid Twin Relate to one Another? Model Order Reduction, Machine Learning, Artificial Intelligence (AI), and the Hybrid Twin approach are interconnected concepts in the field of advanced computational modeling and simulation. ROM provides...
Dr. Fatima DAIM joined ESI Group in 2011 where she is currently a Team Leader in Research and Innovation Department. With her experience in applied mathematics and advanced simulation, she has contributed to the development of model order reduction technologies as well as in the introduction of te...
From the series: Reduced Order Modeling Reduced order modeling (ROM) is a technique for simplifying a high-fidelity mathematical model by reducing its computational complexity while preserving the dominant behavior of the complex model. One common application of reduced order modeling enables s...
Reduced Order Modeling Technique for Beam with Point Load- Example Reduce Model Order Using the Model Reducer App- Example Data-Driven Reduced Order Modeling Reduced Order Model of a Jet Engine Turbine Blade - Example Generate a Deep Learning SI Engine Model- Example ...
Model order reduction, the process of creating a reduced order model, or ROM, is a way of turning performance data obtained through a simulation or test campaign into a super-fast prediction machine. You put parameter in one side and get instant performance metrics out the other. ...
Reduced order modeling (ROM) is a technique for simplifying a high-fidelity mathematical model by reducing its computational complexity while preserving the dominant behavior of the complex model. One common application of reduced order modeling enables simulation of third-party FEA/FEM/CFD mode...
We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC–AE) has been shown to capture nonlinear solution manifolds but fails to perform adequa...