Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions

15 November 2022

Papers citing "Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions"

7 / 7 papers shown

Title
Explaining neural network predictions of material strength Ian Palmer T. Nathan Mundhenk B. J. Gallagher Yong Han 14 2 0 05 Nov 2021
From Show to Tell: A Survey on Deep Learning-based Image Captioning Matteo Stefanini Marcella Cornia Lorenzo Baraldi S. Cascianelli G. Fiameni Rita Cucchiara 3DV VLM MLLM 51 244 0 14 Jul 2021
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design Zijiang Yang Dipendra Jha Arindam Paul W. Liao A. Choudhary Ankit Agrawal MedIm AI4CE 13 9 0 26 Jan 2021
Deep Reinforcement Learning for Autonomous Driving: A Survey B. R. Kiran Ibrahim Sobh V. Talpaert Patrick Mannion A. A. Sallab S. Yogamani P. Pérez 135 1,599 0 02 Feb 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 243 9,042 0 06 Jun 2015
A Large Population Size Can Be Unhelpful in Evolutionary Algorithms Tianshi Chen Ke Tang Guoliang Chen Xin Yao 57 55 0 11 Aug 2012