Oct 6, 2023 · By using nine different three-dimensional chaotic model systems and the high-dimensional spatiotemporal chaotic Kuramoto–Sivashinsky system, we demonstrate that all hybrid reservoir computing approaches significantly improve the prediction results, provided that the model is sufficiently accurate.

Oct 1, 2021 · In this paper, we not only integrate the advantages of active and passive sensors (RGB-D) but also account for the accuracy and automation of 3D modelling, avoid the redundancy of point cloud data and realize data compression;

Dec 22, 2023 · In this study, we explore the effectiveness of a hybrid modelling approach that seamlessly integrates data-driven techniques, specifically Machine Learning (ML), with physics-based equations in Simulation.

Jun 20, 2023 · In this study, we investigate in detail the predictive capabilities of three different architectures for hybrid reservoir computing: the input hybrid (IH), output hybrid (OH), and full hybrid (FH)...

Apr 1, 2022 · Recognizing the complementary strengths of pure physics-based and data-driven models, hybrid physics-based data-driven models are categorized as consisting of three types: (1) physics-informed machine learning, (2) machine learning-assisted simulation, and (3) explainable artificial intelligence.

In this paper, we divide the coupling methods into three categories: 1) data-driven and model-driven cascading methods; 2) variational models with embedded learning; and 3) model-constrained network learning methods.

In this study, we explore the effectiveness of a hybrid mod-elling approach that seamlessly integrates data-driven techniques, specif-ically Machine Learning (ML), with physics-based equations in Simula-tion.

Nov 1, 2019 · In this paper we review the application of data-driven modeling and model learning procedures to different fields in science and engineering. 1. Introduction.

1 day ago · There is a high interest in accelerating multiscale models using data-driven surrogate modeling techniques. Creating a large training dataset encompassing all relevant load scenarios is essential for a good surrogate, yet the computational cost of producing this data quickly becomes a limiting factor. Commonly, a pre-trained surrogate is used throughout the computational domain. Here, we ...

Jan 1, 2007 · After an extensive state of the art, this paper confronts methods belonging to each type of automatic building construction approach. Based on a concrete experiment, the essential points...