DETC2019-97010 Towards Scalable Gaussian Process Modeling

von Pandita, Piyush
Veröffentlicht in Design Automation Conference (45. : 2019 : Anaheim, Calif.) Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - 2019 ; Volume 2 (2020)

DETC2022-87995 Reinforcement Learning Based Sequential Batch-Sampling for Bayesian Optimal Experimental Design

von Ashenafi, Yonatan
Veröffentlicht in ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (2022 : Saint Louis, Mo.) Proceedings of ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE 2022) ; Volume 2: 42nd Computers and Information in Engineering Conference (CIE) (2022)

COMPUTATIONALLY EFFICIENT BAYESIAN OPTIMIZATION FOR MULTI-OBJECTIVE INDUSTRIAL APPLICATIONS

von Pandita, Piyush
Veröffentlicht in AIAA SciTech Forum and Exposition (2021 : Online) Multidisciplinary design optimization ; Volume 2 of 2 (2021)

DIMENSIONALITY REDUCTION FOR MULTI-FIDELITY GAUSSIAN PROCESSES USING BAYESIAN ADAPTATION

von Tsilifis, Panagiotis
Veröffentlicht in AIAA SciTech Forum and Exposition (2021 : Online) Multidisciplinary design optimization ; Volume 2 of 2 (2021)

EFFICIENT BAYESIAN INVERSE METHOD USING ROBUST GAUSSIAN PROCESSES FOR DESIGN UNDER UNCERTAINTY

von Ghosh, Sayan
Veröffentlicht in AIAA SciTech Forum and Exposition (2020 : Orlando, Fla.) Non-deterministic approaches (2020)

UNCERTAINTY QUANTIFICATION OF MESOSCALE MELT-POOL MODEL FOR POWDER BED FUSION ADDITIVE MANUFACTURING OF METALS

von Ghosh, Sayan
Veröffentlicht in AIAA SciTech Forum and Exposition (2021 : Online) Non-deterministic approaches (2021)

Probabilistic Transfer Learning Through Ensemble Probabilistic Deep Neural Network

von Ravi, Sandipp Krishnan
Veröffentlicht in AIAA SciTech Forum (2023 : National Harbor, Md.; Online) Structural dynamics ; Volume 2 of 3 (2023)

Probabilistic Transfer Learning Through Ensemble Probabilistic Deep Neural Network

von Ravi, Sandipp Krishnan
Veröffentlicht in AIAA SciTech Forum (2023 : National Harbor, Md.; Online) Guidance, navigation, and control ; Volume 2 of 4 (2023)

UNCERTAINTY QUANTIFICATION OF MESOSCALE MELT-POOL MODEL FOR POWDER BED FUSION ADDITIVE MANUFACTURING OF METALS

von Ghosh, Sayan
Veröffentlicht in AIAA SciTech Forum and Exposition (2021 : Online) Materials (2021)

Towards Sparse and Interpretable Classification for Damage Assessment of Jet Engine Components

von Tsilifis, Panagiotis
Veröffentlicht in ASME Turbo Expo (2023 : Boston, Mass.) Proceedings of ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition (GT 2023) ; Volume 11, B: Structures and dynamics: Emerging methods in engineering design, analysis, and additive manufacturing; fatigue, fracture, and life prediction; probabilistic methods; rotordynamics; structural mechanics and vibration (2023)

GT2021-58842 A Probabilistic Machine Learning Framework for Explicit Inverse Design of Industrial Gas Turbine Blades

von Ghosh, Sayan
Veröffentlicht in ASME Turbo Expo (2021 : Online) Proceedings of ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition ; Volume 9, B: Structures and dynamics: Fatigue, fracture, and life prediction; probabilistic methods; rotordynamics; structural mechanics and vibration (2021)

PRO-ML IDEAS: A PROBABILISTIC FRAMEWORK FOR EXPLICIT INVERSE DESIGN USING INVERTIBLE NEURAL NETWORK

von Ghosh, Sayan
Veröffentlicht in AIAA SciTech Forum and Exposition (2021 : Online) Multidisciplinary design optimization ; Volume 1 of 2 (2021)

PRO-ML IDEAS: A PROBABILISTIC FRAMEWORK FOR EXPLICIT INVERSE DESIGN USING INVERTIBLE NEURAL NETWORK

von Ghosh, Sayan
Veröffentlicht in AIAA SciTech Forum and Exposition (2021 : Online) Applied aerodynamics ; Volume 2 of 5 (2021)

PRO-ML IDEAS: A PROBABILISTIC FRAMEWORK FOR EXPLICIT INVERSE DESIGN USING INVERTIBLE NEURAL NETWORK

von Ghosh, Sayan
Veröffentlicht in AIAA SciTech Forum and Exposition (2021 : Online) Non-deterministic approaches (2021)