Robust Design

9 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Towards Robust Vision Transformer

alibaba/easyrobust CVPR 2022

By using and combining robust components as building blocks of ViTs, we propose Robust Vision Transformer (RVT), which is a new vision transformer and has superior performance with strong robustness.

Inverse deep learning methods and benchmarks for artificial electromagnetic material design

bensonren/aem_dim_bench 19 Dec 2021

Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices.

Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory

ArashRahnama/RobustLyapunovDNNs CVPR 2020

We treat each individual layer of the DNN as a nonlinear dynamical system and use Lyapunov theory to prove stability and robustness locally.

Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions

LorenzoFederici/RobustTrajectoryDesignbyRL 19 Aug 2020

This paper investigates the use of Reinforcement Learning for the robust design of low-thrust interplanetary trajectories in presence of severe disturbances, modeled alternatively as Gaussian additive process noise, observation noise, control actuation errors on thrust magnitude and direction, and possibly multiple missed thrust events.

Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes

Data-Science-in-Mechanical-Engineering/prlqr 17 May 2021

Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design.

Exploring Robust Architectures for Deep Artificial Neural Networks

Waasem/RobDanns 30 Jun 2021

The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance.

Local Latin Hypercube Refinement for Multi-objective Design Uncertainty Optimization

canbooo/duqo 19 Aug 2021

Optimizing the reliability and the robustness of a design is important but often unaffordable due to high sample requirements.

RID-Noise: Towards Robust Inverse Design under Noisy Environments

thyrixyang/rid-noise-aaai22 7 Dec 2021

We also define a sample-wise weight, which can be used in the maximum weighted likelihood estimation of an inverse model based on a cINN.

GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

wchen459/gan-duf 21 Feb 2022

To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design.