Robust Design

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Most implemented papers archive 2025-07-28

14 shown of 14 papers with code (74 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Inverse deep learning methods and benchmarks for artificial electromagnetic material design
  19 Dec 2021 2 repositories listed
  Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices.
• Towards Robust Vision Transformer
  17 May 2021 2 repositories listed
  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.
• NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
  20 Mar 2025 1 repository listed
  This work discusses the methodology and performance of NeuralFoil with several case studies, including a practical airfoil design optimization study including both aerodynamic and non-aerodynamic constraints.
• Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation
  10 Dec 2024 1 repository listed
  This paper deals with state estimation of nonlinear stochastic dynamic models.
• Latent Causal Probing: A Formal Perspective on Probing with Causal Models of Data
  18 Jul 2024 1 repository listed
  As language models (LMs) deliver increasing performance on a range of NLP tasks, probing classifiers have become an indispensable technique in the effort to better understand their inner workings.
• Robust Waveform Design for Integrated Sensing and Communication
  31 Oct 2023 1 repository listed
  Simulation results show that by solving the robust waveform design problems, the lower bound of the true but unknown Pareto frontier, which characterizes the sensing-communication performance trade-off under…
• GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
  21 Feb 2022 1 repository listed
  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…
• RID-Noise: Towards Robust Inverse Design under Noisy Environments
  7 Dec 2021 1 repository listed
  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
  19 Aug 2021 1 repository listed
  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
  30 Jun 2021 1 repository listed
  The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance.
• Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes
  17 May 2021 1 repository listed
  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
  19 Aug 2020 1 repository listed
  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,…
• Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
  12 Nov 2019 1 repository listed
  We treat each individual layer of the DNN as a nonlinear dynamical system and use Lyapunov theory to prove stability and robustness locally.
• Conditioning by adaptive sampling for robust design
  29 Jan 2019 1 repository listed
  We assume access to one or more, potentially black box, stochastic "oracle" predictive functions, each of which maps from input (e.