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AdsorbML: Accelerating Adsorption Energy Calculations with Machine Learning
[article]
2023
arXiv
pre-print
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In this paper, we demonstrate machine learning potentials can be leveraged to identify low energy adsorbate-surface configurations more accurately and efficiently. ...
Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. ...
This would enable us to increase the number of random placements without the concern of redundant calculations. ...
arXiv:2211.16486v2
fatcat:p52n6cuepfbrhkbl7kbxb36om4
Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning
[article]
2022
arXiv
pre-print
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The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. ...
learning for high energy physics. ...
Data reconstruction and analysis Incorporating domain knowledge, also referred to as inductive bias, for data reconstruction and analysis into a machine learning solution can provide significant benefits ...
arXiv:2209.07559v1
fatcat:txa7vf2vibgrdivnpmco4rre5i
N-SfC: Robust and Fast Shape Estimation from Caustic Images
[article]
2021
arXiv
pre-print
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the computational cost of the light transport simulation, and an optimization process based on learned gradient descent, which enables better convergence using fewer iterations. ...
However, the inherent complexity of light transport through refracting surfaces currently limits the practicability with respect to reconstruction speed and robustness. ...
This approach achieves high quality results of free form shapes, but the physical setup complexity is beyond integration in existing manufacturing machines and the scope of this paper. ...
arXiv:2112.06705v1
fatcat:vkrok2rkv5h4doczc4olpeai5i
A State-of-the-art Survey of Advanced Optimization Methods in Machine Learning
2021
International Conference on Recent Trends and Applications in Computer Science and Information Technology
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It starts with a short introduction to machine learning followed by the formulation of optimization problems in three main approaches to machine learning. ...
The main objective of this paper is to provide a state-of-the-art survey of advanced optimization methods used in machine learning. ...
We hope that the issues discussed in this paper will push forward the discussion in the area of optimization and machine learning, on the same time it may serve as complementary material for other researchers ...
dblp:conf/rtacsit/KastratiB21
fatcat:gcvz6va2wrdgvcorfb52qdac4q
Multi-Domain Informative Coverage Path Planning for A Hybrid Aerial Underwater Vehicle in Dynamic Environments
2021
Machines
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We introduce the novel heuristic generalized extensive neighborhood search GLNS–k-means algorithm that uses k-means to cluster information into several sets; then through the heuristic GLNS algorithm, ...
The proposed replanning scheme based on KD tree enables significantly shorter computational times than the scapegoat tree methods. ...
We update the treTree with the scapegoat tree, or simply reconstruct another KD tree obTree with points on the obstacle's surface. ...
doi:10.3390/machines9110278
fatcat:qmi7fmthmjgn7lzmvfom6skox4
Supervised footstep planning for humanoid robots in rough terrain tasks using a black box walking controller
2014
2014 IEEE-RAS International Conference on Humanoid Robots
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A proper height map and surface normal estimation are directly obtained from point cloud data. A search-based planning approach (ARA*) is extended to sequences of footsteps in full 3D space (6 DoF). ...
In this paper we present a complete system for supervised footstep planning including perception, world modeling, 3D planner and operator interface to enable a humanoid robot to perform sequences of steps ...
The authors would like to thank all members of Team ViGIR for their contribution and support which enabled the realization of this work. ...
doi:10.1109/humanoids.2014.7041374
dblp:conf/humanoids/StumpfKCS14
fatcat:exfuyimbwrhalfqkrecjiraoc4
Lagrangian Large Eddy Simulations via Physics Informed Machine Learning
[article]
2022
arXiv
pre-print
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Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. ...
Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). ...
Financial support comes from Los Alamos National Laboratory (LANL), Laboratory Directed Research and Development (LDRD) project "Machine Learning for Turbulence," 20180059DR. ...
arXiv:2207.04012v2
fatcat:unlj3zfnzffatcxc44z2dijda4
Coarse-graining auto-encoders for molecular dynamics
2019
npj Computational Materials
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Molecular dynamics simulations provide theoretical insight into the microscopic behavior of condensed-phase materials and, as a predictive tool, enable computational design of new compounds. ...
Coarse-graining involves two coupled learning problems: defining the mapping from an all-atom representation to a reduced representation, and parameterizing a Hamiltonian over coarse-grained coordinates ...
term to facilitate the learning of a smooth coarse-grained free-energy surface and to average out fast dynamics. ...
doi:10.1038/s41524-019-0261-5
fatcat:x2n2ha526bbtvmhi32pm5a6arq
Navigating the Landscape for Real-Time Localization and Mapping for Robotics and Virtual and Augmented Reality
2018
Proceedings of the IEEE
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design space with respect to multiple objectives, (3) end-to-end simulation tools to enable optimisation of heterogeneous, accelerated architectures for the specific algorithmic requirements of the various ...
The major contributions we present are (1) tools and methodology for systematic quantitative evaluation of SLAM algorithms, (2) automated, machine-learning-guided exploration of the algorithmic and implementation ...
Then live dense reconstruction methods, dense tracking and mapping (DTAM) using a standard single camera [6] and KinectFusion using a Microsoft Kinect depth camera [7] showed that surface reconstruction ...
doi:10.1109/jproc.2018.2856739
fatcat:a66m7lzvn5bjvlxyw7qkd2qaky
IEEE Access Special Section Editorial: Scalable Deep Learning for Big Data
2020
IEEE Access
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Unlike traditional machine learning (ML) approaches, DL also enables dynamic discovery of features from data. ...
Experimental results show that the proposed method can accurately reconstruct the surface shape of the point cloud model and could reflect the detailed features of the model more naturally. ...
doi:10.1109/access.2020.3041166
fatcat:zkzdnzk22jge3l5mwju3j42mcu
Inferring CAD Modeling Sequences Using Zone Graphs
[article]
2021
arXiv
pre-print
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We show that our approach outperforms an existing CSG inference baseline in terms of geometric reconstruction accuracy and reconstruction time, while also creating more plausible modeling sequences. ...
We phrase our problem as search in the space of such extrusions permitted by the zone graph, and we train a graph neural network to score potential extrusions in order to accelerate the search. ...
Acknowledgments We would like to thank Justin Solomon for pointing us toward the literature on arrangements of surfaces within computational geometry. ...
arXiv:2104.03900v2
fatcat:y3ssgfypmbckdcbgfz5gffwfga
Machine Learning for a Sustainable Energy Future
[article]
2022
arXiv
pre-print
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Researchers globally have begun incorporating machine learning (ML) techniques with the aim of accelerating these advances. ...
We then introduce a set of key performance indicators to help compare the benefits of different ML-accelerated workflows for energy research. ...
Researchers globally have begun incorporating machine learning (ML) techniques with the aim of accelerating these advances. ...
arXiv:2210.10391v1
fatcat:bawalpzicvh55posjyecsmhkwq
DiffSRL: Learning Dynamical State Representation for Deformable Object Manipulation with Differentiable Simulator
[article]
2022
arXiv
pre-print
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Latent space that can capture dynamics related information has wide application in areas such as accelerating model free reinforcement learning, closing the simulation to reality gap, as well as reducing ...
We propose DiffSRL, a dynamic state representation learning pipeline utilizing differentiable simulation that can embed complex dynamics models as part of the end-to-end training. ...
model free reinforcement learning. ...
arXiv:2110.12352v2
fatcat:rtdu2v2nznf23alsgznc7vvw4e
Learning Physically Realizable Skills for Online Packing of General 3D Shapes
[article]
2022
arXiv
pre-print
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Equipped with an efficient method of asynchronous RL acceleration and a data preparation process of simulation-ready training sequences, a mature packing policy can be trained in a physics-based environment ...
We propose a Reinforcement Learning (RL) pipeline to learn the policy. The complex irregular geometry and imperfect object placement together lead to huge solution space. ...
This method significantly reduces the search space of RL and enables reliable learning of packing policies. ...
arXiv:2212.02094v1
fatcat:6hsdhetfs5gelor3w3ykzxjdeq
State of the Art on Neural Rendering
[article]
2020
arXiv
pre-print
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Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. ...
Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. ...
It enables machines to learn to perceive their surroundings based on a representation and generation network. ...
arXiv:2004.03805v1
fatcat:6qs7ddftkfbotdlfd4ks7llovq
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