Robust Design
11 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Robust Design
Latest papers
Robust Waveform Design for Integrated Sensing and Communication
Therefore, we formulate robust waveform design problems by studying the worst-case channels and prove that the robustly-estimated performance is guaranteed to be attainable in real-world operation.
GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
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.
Inverse deep learning methods and benchmarks for artificial electromagnetic material design
Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices.
RID-Noise: Towards Robust Inverse Design under Noisy Environments
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.
Local Latin Hypercube Refinement for Multi-objective Design Uncertainty Optimization
Optimizing the reliability and the robustness of a design is important but often unaffordable due to high sample requirements.
Exploring Robust Architectures for Deep Artificial Neural Networks
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance.
Towards Robust Vision Transformer
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.
Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes
Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design.
Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions
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.
Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
We treat each individual layer of the DNN as a nonlinear dynamical system and use Lyapunov theory to prove stability and robustness locally.