surrogate model

深層カーネル学習代替モデルによる高架橋システムの地震フラジリティ解析

Seismic fragility analysis of structures such as bridges must consider not only the uncertainties in the loads to which the structure is subjected, but also the uncertainties in the physical properties of the structure due to deterioration and other …

橋梁の地震フラジリティ解析効率化のためのガウス過程回帰代替モデル構築(招待講演)

Gaussian Process Regression Surrogate Model for Seismic Vulnerability Assessment of Highway Bridge Structure System

System fragility is required especially for the vulnerability assessment of existing bridges that configure transportation network as a structural system. Here, it is essential to consider not only variation of input ground motions but also the …

高次元不確定性を扱う構造信頼性解析を効率化するガウス過程ベース代替モデル構築(招待講演)

PINN構造振動解析のARによるリアルタイム可視化

Real-time simulation of structures and structural members enables intuitive and immediate understanding of displacements and stresses, contributing to efficient decision making in design and maintenance. In this study, real-time simulation of …

SPH法に基づく微分演算を内包した深層学習による粒子法代替モデルの説明性向上

The particle method does not require any computational grid and is an effective numerical method for simulating behaviors of the continuum mechanics. However, the computational cost is the issue to apply it to more complex physics or to the Monte …

深層カーネル学習サロゲートモデルによる高次元不確定性をもつ構造信頼性解析の効率化

Bridge systems have many components, and their fragility assessment must consider high-dimensional uncertainties. This study constructed a deep kernel learning (DKL) surrogate model to reduce the computational cost of fragility analysis of bridge …

地震荷重特徴抽出を備えた深層カーネル学習代替モデルによる地震リスク解析の効率化(シンポジウム講演概要)

本研究では、地震リスク解析の計算コストを削減するために、深層カーネル学習による代替モデルを開発した。このモデルは、畳み込みニューラルネットワーク(CNN)を用いて地震荷重の特徴を抽出する。さらに、Gradient-weighted Class Activation Mapping(Grad-CAM)により地震荷重の各部の寄与を推定し、ARDにより各構造パラメータの寄与を推定することで、代替モデルの説明可能性を高める。検証では、免震RC橋脚の地震応答解析のために代替モデルを構築した。その結果、代替 …

TL-GPRSM: A python software for constructing transfer learning Gaussian process regression surrogate model with explainability

Software Impacts (**Impact Factor: 2.1** in 2022)

Transfer learning Gaussian process regression surrogate model with explainability for structural reliability analysis under variation in uncertainties

Computers & Structures (**Impact Factor: 5.372** in 2021; [Structural Engineering at Google Scholar Metrics](https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_structuralengineering))