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Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior

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Gaussian Process Port-Hamiltonian Systems: Bayesian Learning...

IEEE Conference on Decision and Control

作者： Thomas Beckers Jacob Seidman Paris Perdikaris George J. Pappas Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA graduate program in Applied Mathematics and Computational Science University of Pennsylvania Philadelphia PA USA Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data. However, these models often neglect basic physical principles which determine the behavior of any real-world system. This omission is unfavorable in two ways: The models are not as data-efficient as they could be by incorporating physical prior knowledge, and the model itself might not be physically correct. We propose Gaussian Process Port-Hamiltonian systems (GPPHS) as a physics-informed Bayesian learning approach with uncertainty quantification. The Bayesian nature of GP-PHS uses collected data to form a distribution over all possible Hamiltonians instead of a single point estimate. Due to the underlying physics model, a GP-PHS generates passive systems with respect to designated inputs and outputs. Further, the proposed approach preserves the compositional nature of Port-Hamiltonian systems.

关键词： Uncertainty Gaussian processes Probabilistic logic Data models Bayes methods Behavioral sciences Kernel

Comparing LSTM and BLSTM Deep Neural Networks for Power Consumption Prediction: Preliminary studies

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arXiv 2023年

作者： da Silva, Davi Guimarães de Moura Meneses, Anderson Alvarenga Federal University of Western Pará – Graduate Program in Society Nature and Development R. Vera Paz s/n Salé PA SantarémCEP 68.035-110 Brazil Federal Institute of Education Science and Technology of Pará Brazil Federal University of Western Pará Institute of Geosciences and Engineering Laboratory of Computational Intelligence Brazil

Electric consumption prediction methods are investigated for many reasons such as decision-making related to energy efficiency as well as for anticipating demand in the energy market dynamics. The objective of the present work is the comparison between two Deep Learning models, namely the Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast. The Data Sets (DSs) were selected for their different contexts and scales, aiming the assessment of the models’ robustness. Four DSs were used, related to the power consumption of: (a) a household in France;(b) a university building in Santarém, Brazil;(c) the Tétouan city zones, in Morocco;and (c) the Singapore aggregated electric demand. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. The Friedman’s test was applied to normalized RMSE (NRMSE) results, showing that BLSTM outperforms LSTM with statistically significant difference (p = 0.0455), corroborating the fact that bidirectional weight updating improves significantly the LSTM performance concerning different scales of electric power consumption. © 2023, CC BY-NC-ND.

关键词： Long short-term memory

DIRECTED SCATTERING FOR KNOWLEDGE GRAPH-BASED CELLULAR SIGNALING ANALYSIS

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arXiv 2023年

作者： Venkat, Aarthi Chew, Joyce Rodriguez, Ferran Cardoso Tape, Christopher J. Perlmutter, Michael Krishnaswamy, Smita Program for Computational Biology and Bioinformatics Yale University United States Department of Mathematics University of California Los Angeles United States Department of Oncology University College London Cancer Institute United Kingdom Department of Mathematics Boise State University United States Department of Genetics and Computer Science Yale University United States

Directed graphs are a natural model for many phenomena, in particular scientific knowledge graphs such as molecular interaction or chemical reaction networks that define cellular signaling relationships. In these situations, source nodes typically have distinct biophysical properties from sinks. Due to their ordered and unidirectional relationships, many such networks also have hierarchical and multiscale structure. However, the majority of methods performing node- and edge-level tasks in machine learning do not take these properties into account, and thus have not been leveraged effectively for scientific tasks such as cellular signaling network inference. We propose a new framework called Directed Scattering Autoencoder (DSAE) which uses a directed version of a geometric scattering transform, combined with the non-linear dimensionality reduction properties of an autoencoder and the geometric properties of the hyperbolic space to learn latent hierarchies. We show this method outperforms numerous others on tasks such as embedding directed graphs and learning cellular signaling networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Directed graphs

Embedding and trajectories of temporal networks

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arXiv 2022年

作者： Thongprayoon, Chanon Livi, Lorenzo Masuda, Naoki Department of Mathematics State University of New York at Buffalo BuffaloNY United States Department of Computer Science University of Manitoba WinnipegMB Canada Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States Faculty of Science and Engineering Waseda University Shinjuku Tokyo Japan

Temporal network data are increasingly available in various domains, and often represent highly complex systems with intricate structural and temporal evolutions. Due to the difficulty of processing such complex data, it may be useful to coarse grain temporal network data into a numeric trajectory embedded in a low-dimensional space. We refer to such a procedure as temporal network embedding, which is distinct from procedures that aim at embedding individual nodes. Temporal network embedding is a challenging task because we often have access only to discrete time-stamped events between node pairs, and, in general, the events occur with irregular intervals, making the construction of the network at a given time a nontrivial question already. We propose a method to generate trajectories of temporal networks embedded in a low-dimensional space given a sequence of time-stamped events as input. We realize this goal by combining the landmark multidimensional scaling, which is an out-of-sample extension of the well-known multidimensional scaling method, and the framework of tie-decay temporal networks. This combination enables us to obtain a continuous-time trajectory describing the evolution of temporal networks. We then study mathematical properties of the proposed temporal network embedding framework. Finally, we showcase the method with empirical data of social contacts to find temporal organization of contact events and loss of them over a single day and across different days. © 2022, CC BY.

关键词： Trajectories

Molecular docking study of inclusion complex between aromatic amine and Calixarene analogs 2

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Molecular docking study of inclusion complex between aromati...

2nd Annual Conference of science and Technology, ANCOSET 2020

作者： Ivansyah, A.L. Master Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl. Ganesha No. 10 Bandung Indonesia Analytical Chemistry Research Group Department of Chemistry Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl. Ganesha No. 10 Bandung Indonesia

A molecular docking was performed in this research by using ArgusLab version 4.0.1. It was used to explore the capability of Calixarene analogs for aromatic amine extraction. There are 6 Calixarene analogs used in this research, i.e. Calixpyrrole, Calixpyridine, Thiacalixarene, Heterocalixaromatic, Calixcarbazole and Calixnaphtalene;and the aromatic amine used in this research are Aniline, 4-Chloroaniline, Toluene-2,4-diamine, 2-Naphtylamine, 4,4'-Metylenbis(2-chloroaniline), 4,4'-Metylendianiline, N-Nitrosodiphenylamine, Benzidine, 2-Aminobiphenyl, 2-Amino-1-methyl-6-phenylimidazo [4,5-b] Pyridine, 3-Trifluoromethylaniline, p-Phenylendiamine, o-Toluidin, 4-Chloro-o-toluidin. In this molecular docking simulation, the guest molecule of aromatic amine compounds were treated as ligand and host molecule of calixarene and its analogs were treated as binding site. This molecular docking simulation used AScore scoring function, binding sitebox size for host molecule with grid size of 0.4 Å, GA docking engine with flexible ligand, 50 population sizes, 1000 max generations, 5 elitisms, 0.2 mutation rate, and 0.8 crossover rate. Calixcarbazole and Calixnaphthelene is the best host molecule for aromatic amines extraction because the inclusion complexes formed between them have negative value of binding energy. © Published under licence by IOP Publishing Ltd.

关键词： Binding energy

computational modeling of inclusion complex between aromatic amine and calixarene analogs using Semiempiric Quantum Method (SQM) 2

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Computational modeling of inclusion complex between aromatic...

2nd Annual Conference of science and Technology, ANCOSET 2020

PM3 Semiempiric Quantum calculation has been done in this research by using Gaussian 2009 Rev. D 01 software package to explore the capability of Calixarene and its analogs for aromatic amine extraction. The Calixarene analogs used in this research are Calixpyrrole, Calixpyridine, Thiacalixarene, Heterocalixaromatic, Calixcarbazole and Calixnaphtalene;and the aromatic amine used in this research are Aniline, 4-Chloroaniline, Toluene-2,4-diamine, 2-Naphty lamině, 4,4'-Mety lenbis(2-chloroaniline), 4,4'-Metylendianiline, N-Nitrosodiphenylamine, Benzidine, 2-Aminobiphenyl, 2-Amino-1-methyl-6-phenylimidazo[4,5-b] Pyridine, 3-Trifluoromethylaniline, p-Phenylendiamine, o-Toluidin, 4-Chloro-o-toluidin. The most suitable host molecule of calixarene analogs for extraction of aromatic amine compounds are Calixnaphthalene and Calixpyridine because all of aromatic amine compounds form inclusion complex with them spontaneously shown by negative value of binding energy. © Published under licence by IOP Publishing Ltd.

关键词： Aniline

High-frequency aware perceptual image enhancement

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arXiv 2021年

作者： Roh, Hyungmin Kang, Myungjoo The Interdisciplinary Program of Computational Science and Technology Seoul National University Seoul Korea Republic of Department of Mathematics Seoul National University Seoul Korea Republic of

In this paper, we introduce a novel deep neural network suitable for multi-scale analysis and propose efficient model-agnostic methods that help the network extract information from high-frequency domains to reconstruct clearer images. Our model can be applied to multi-scale image enhancement problems including denoising, deblurring and single image super-resolution. Experiments on SIDD, Flickr2K, DIV2K, and REDS datasets show that our method achieves state-of-the-art performance on each task. Furthermore, we show that our model can overcome the over-smoothing problem commonly observed in existing PSNR-oriented methods and generate more natural high-resolution images by applying adversarial training. © 2021, CC BY.

关键词： Image enhancement

DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

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arXiv 2022年

作者： Li, Wenfei Ou, Qi Chen, Yixiao Cao, Yu Liu, Renxi Zhang, Chunyi Zheng, Daye Cai, Chun Wu, Xifan Wang, Han Chen, Mohan Zhang, Linfeng AI for Science Institute Beijing100080 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States HEDPS CAPT College of Engineering School of Physics Peking University Beijing100871 China Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China DP Technology Beijing100080 China

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), a ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely-matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model, and then use the DeePKS model to label a much larger amount of configurations to train a ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open-source and ready for use in various applications. © 2022, CC BY.

关键词： Density functional theory

Biogas production and its utilization in internal combustion engines - A review

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Process Safety and Environmental Protection 2024年 186卷 518-539页

作者： Vasan, Vinod Sridharan, Naveen Venkatesh Feroskhan, M. Vaithiyanathan, Sugumaran Subramanian, Balaji Tsai, Pei-Chien Lin, Yuan-Chung Lay, Chyi-How Wang, Chin-Tsan Ponnusamy, Vinoth Kumar Vellore Institute of Technology Chennai600127 India Department of Mechanical Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Kaohsiung City807 Taiwan Department of Computational Biology Institute of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Institute of Environmental Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Center for Emerging Contaminants Research National Sun Yat-sen University Kaohsiung804 Taiwan Master's Program of Green Energy Science and Technology Feng Chia University Taichung City407102 Taiwan Department of Mechanical and Electro-Mechanical Engineering National I-Lan University I Lan Taiwan Research Center for Precision Environmental Medicine Kaohsiung Medical University Kaohsiung City807 Taiwan Kaohsiung City807 Taiwan Division of Operation and Maintenance Engineering Luleå University of Technology Luleå Sweden Department of Chemistry National Sun Yat-sen University Kaohsiung804 Taiwan

This review presents the significance and current challenges associated with fossil fuels, highlighting the need for cleaner and more sustainable energy sources. The significant object of the current study is to concentrate on increased interest in utilizing biogas as a viable substitute for traditional fuels, delving into its characteristics, benefits, accessibility, feasibility, and prospects as an environmentally sustainable energy option. The current study extensively analyses the prospect and significance of biogas in mitigating emissions regulations in Internal Combustion Engines (ICEs). This paper provides a comprehensive overview of recent research on incorporating biogas in internal combustion engines, focusing on emission reduction and the corresponding impact on engine performance. The subject matter encompasses a range of facets about biogas, encompassing its origins, chemical composition, purification, enhancement techniques, and requisite engine adjustments to enable the effective utilization of biogas as an alternative fuel. The study also evaluates the performance of biogas-fuelled ICEs based on the results of cross-referencing with contemporary research and investigates peripheral aspects such as additive inclusion to increase biogas capabilities as fuel and potential applications in advanced engine modes of operation. The review identifies research gaps in the domains of biogas processing and utilization in ICEs and acknowledges existing challenges specific to the adoption of biogas as a conventional fuel alternative. Future research directions are also prescribed, further highlighting the importance of transitioning from fossil fuels to meet environmental goals and global energy demands. © 2024 The Institution of Chemical Engineers

关键词： Biogas