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, ...
模型降阶(Model Order Reduction)、机器学习(Machine Learning)、人工智能(Artificial Intelligence, AI)以及混合孪生方法(Hybrid Twin approach)在高级计算建模与仿真领域中是相互关联的概念。 模型降阶(ROM)提供了复杂系统模型的简化但准确的表示,具有计算效率高的优势。机器学习和混合人工智能增强了模型降阶,使...
Reduced order modeling (ROM) is a technique to simplify a high-fidelity mathematical model by reducing its computational complexity while preserving the dominant behavior of the complex model. This series highlights different applications of ROM and meth
Physics-informed machine learning of reduced-order model without requirement of extra high-fidelity snapshots. • A PINN trained by minimizing the residual loss of the reduced-order equation. • A PRNN with improved accuracy obtained by adding the regression loss on the available high-fidelity ...
Deep learning order reduction Another ROM technique consists in creating a surrogate model using a machine learning approach. Surrogate models are usually developed because of their significant performance advantage over more detailed, application- or discipline- specific (e.g., physics-based) model i...
A physics-informed machine learning framework is developed for the reduced-order modeling of parametrized steady-state partial differential equations (PDEs). During the offline stage, a reduced basis that represents the...
Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-based reduction, such as Proper Orthogonal Decomposition. The limitation o
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...
Simcenter Reduced Order Modeling guides you through the end-to-end ROM creation process. A single interface manages the workflow from data import, through model selection, training and validation, to export. Different workflows are suitable for both experts and non-experts. Not sure which data redu...
To combine the advantages of these two methods for reconstructing the subsurface density field from surface data, a novel dynamics‐constrained deep operator learning network based on reduced‐order model is proposed. Encoding the mean‐squared residuals of the reduced‐order equation along with the ...