[HTML][HTML] Additive manufacturing of FeCrAl alloys for nuclear applications-A focused review
FeCrAl alloys exhibit outstanding high-temperature oxidation resistance and impressive
mechanical strength, rendering them as forefront materials with broad applicability across …
mechanical strength, rendering them as forefront materials with broad applicability across …
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 …
Towards physics-informed explainable machine learning and causal models for materials research
A Ghosh - Computational Materials Science, 2024 - Elsevier
From emergent material descriptions to estimation of properties stemming from structures to
optimization of process parameters for achieving best performance–all key facets of …
optimization of process parameters for achieving best performance–all key facets of …
Enhancing Part Quality Management Using a Holistic Data Fusion Framework in Metal Powder Bed Fusion Additive Manufacturing
Metal powder bed fusion additive manufacturing (AM) processes have gained widespread
adoption for the ability to produce complex geometries with high performance. However, a …
adoption for the ability to produce complex geometries with high performance. However, a …
Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields
A Samaddar, SK Ravi… - Journal of …, 2024 - asmedigitalcollection.asme.org
Tensor datatypes representing field variables like stress, displacement, velocity, etc have
increasingly become a common occurrence in data-driven modeling and analysis of …
increasingly become a common occurrence in data-driven modeling and analysis of …
Probabilistic transfer learning through ensemble probabilistic deep neural network
View Video Presentation: https://doi. org/10.2514/6.2023-1479. vid Design optimization has
been a long standing endeavor of engineers and designers. With the advent of machine …
been a long standing endeavor of engineers and designers. With the advent of machine …
Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with Sparse Data
A Bhaduri, N Ramachandra… - Journal of …, 2024 - asmedigitalcollection.asme.org
Establishing fast and accurate structure-to-property relationships is an important component
in the design and discovery of advanced materials. Physics-based simulation models like …
in the design and discovery of advanced materials. Physics-based simulation models like …
Physics Discovery of Engineering Applications With Constrained Optimization and Genetic Programming
Discovering physics from data have the potential to advance our understanding and
prediction of a system where the governing physics are unknown but experimental data are …
prediction of a system where the governing physics are unknown but experimental data are …
Interpretable Multi-Source Data Fusion Through Latent Variable Gaussian Process
With the advent of artificial intelligence (AI) and machine learning (ML), various domains of
science and engineering communites has leveraged data-driven surrogates to model …
science and engineering communites has leveraged data-driven surrogates to model …
Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems
L Luan, N Ramachandra… - International …, 2023 - asmedigitalcollection.asme.org
Modern computational methods involving highly sophisticated mathematical formulations
enable several tasks like modeling complex physical phenomena, predicting key properties …
enable several tasks like modeling complex physical phenomena, predicting key properties …