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2211.08179
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Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
15 November 2022
Joseph B. Choi
Phong C. H. Nguyen
O. Sen
H. Udaykumar
Stephen Seung-Yeob Baek
PINN
AI4CE
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Papers citing
"Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions"
9 / 9 papers shown
Title
Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials
Robert Appleton
Daniel J. Klinger
Brian H. Lee
Michael Taylor
Sohee Kim
Samuel Blankenship
Brian C Barnes
Steven F. Son
Alejandro Strachan
18
1
0
21 Aug 2024
Constrained Synthesis with Projected Diffusion Models
Jacob K Christopher
Stephen Baek
Ferdinando Fioretto
DiffM
28
10
0
05 Feb 2024
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
15
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
1