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Surrogate modeling: tricks that endured the test of time and some recent developments
Tasks such as analysis, design optimization, and uncertainty quantification can be
computationally expensive. Surrogate modeling is often the tool of choice for reducing the …
computationally expensive. Surrogate modeling is often the tool of choice for reducing the …
Compressor airfoil optimization method driven by data-mechanism integration based on evolutionary multi-tasking algorithm
J Cheng, Y Zhang, J Chen, H Ma, B Liu - Aerospace Science and …, 2024 - Elsevier
To address the challenge of the" curse of dimensionality" in aerodynamic design
optimization of compressors, this study introduces an innovative optimization technique …
optimization of compressors, this study introduces an innovative optimization technique …
Advances in bayesian probabilistic modeling for industrial applications
Industrial applications frequently pose a notorious challenge for state-of-the-art methods in
the contexts of optimization, designing experiments and modeling unknown physical …
the contexts of optimization, designing experiments and modeling unknown physical …
On Uncertainty Quantification in Materials Modeling and Discovery: Applications of GE's BHM and IDACE
View Video Presentation: https://doi. org/10.2514/6.2023-0528. vid The coupling of artificial
intelligence and materials characterizations has been a center piece of almost all materials …
intelligence and materials characterizations has been a center piece of almost all materials …
Application of deep transfer learning and uncertainty quantification for process identification in powder bed fusion
Accurate identification and modeling of process maps in additive manufacturing remains a
pertinent challenge. To ensure high quality and reliability of the finished product …
pertinent challenge. To ensure high quality and reliability of the finished product …
Pro-ml ideas: A probabilistic framework for explicit inverse design using invertible neural network
View Video Presentation: https://doi. org/10.2514/6.2021-0465. vid An inverse design
process has the potential to positively impact the difficulties of the traditional iterative …
process has the potential to positively impact the difficulties of the traditional iterative …
Reinforcement learning-based sequential batch-sampling for bayesian optimal experimental design
Engineering problems that are modeled using sophisticated mathematical methods or are
characterized by expensive-to-conduct tests or experiments are encumbered with limited …
characterized by expensive-to-conduct tests or experiments are encumbered with limited …
A federated, multimodal digital thread platform for enabling digital twins
The “digital twin” is emerging as a dominant paradigm aimed at improving outcomes
associated with physical counterparts within several industrial sectors. Recently, the …
associated with physical counterparts within several industrial sectors. Recently, the …
Bayesian Optimization for Multi-Objective High-Dimensional Turbine Aero Design
Industrial design fundamentally relies on high-dimensional multi-objective optimization.
Bayesian Optimization (BO) based on Gaussian Processes (GPs) has been shown to be …
Bayesian Optimization (BO) based on Gaussian Processes (GPs) has been shown to be …
A Physics-informed Data-driven Approach to Additive Manufacturing Parameter Optimization
A novel framework including experimental and model-based techniques saves time and
enables the introduction of new alloys for additive manufacturing. This article describes the …
enables the introduction of new alloys for additive manufacturing. This article describes the …