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Empowering Optimal Control with Machine Learning: A Perspective from Model Predictive Control

IFAC-PapersOnLine 2022年第30期55卷 121-126页

作者： E Weinan Jiequn Han Jihao Long AI for Science Institute Beijing Center for Machine Learning Research and School of Mathematical Sciences Peking University Beijing China Center for Computational Mathematics Flatiron Institute New York 10010 USA Program of Applied and Computational Mathematics Princeton University Princeton 08544 USA

Solving complex optimal control problems have confronted computational challenges for a long time. Recent advances in machine learning have provided us with new opportunities to address these challenges. This paper takes model predictive control, a popular optimal control method, as the primary example to survey recent progress that leverages machine learning techniques to empower optimal control solvers. We also discuss some of the main challenges encountered when applying machine learning to develop more robust optimal control algorithms.

关键词： Machine Learning Optimal Control Model Predictive Control

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Effect of Nickel on the Hydroxide Ion Adsorption of Copper Sulfide: A viewpoint from a first-principle calculation 2

Effect of Nickel on the Hydroxide Ion Adsorption of Copper S...

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2nd TALENTA-International Conference on science and Technology, TALENTA-ICST 2020

作者： Irham, M.A. Muttaqien, F. Bisri, S.Z. Iskandar, F. Electronics Material Physics Research Group Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Instrumentation and Computational Physics Research Group Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Master Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia RIKEN Center for Emergent Matter Science 2-1 Hirosawa Saitama Wako351-0198 Japan Research Center for Nanosciences and Nanotechnology Institut Teknologi Bandung Jl. Ganesha 10 Bandung 40132 Indonesia

Supercapacitor gains so much interest in recent years due to its capabilities to provide high power densities with high energy density possibilities. Several transition metal sulfides (TMS) give the best capacitance results nowadays, with Copper Sulfides (CuS) promising candidates on supercapacitor electrodes materials. Several approaches have been made to increase TMS-based supercapacitor performance, such as doping, surface functionalization, and nanostructuring. Even though several dopants already reported, other dopants have not yet explored. In this study, we study the nickel effect as dopant on the binding energy of electrolyte in the surface of CuS from first-principles viewpoints. In this study, we limit our investigation of surface interaction with electrolyte only for OH- ions. We prove that the binding energy of electrolyte is increased after introducing dopant in the surface of CuS, which could explain of dopant effect and background proof of future research in this field. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Copper compounds

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Directed Scattering for Knowledge Graph-Based Cellular Signaling Analysis

Directed Scattering for Knowledge Graph-Based Cellular Signa...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

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

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.

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Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

arXiv

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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion

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Transcription Factor-Centric Approach to Identify Non-recurring Putative Regulatory Drivers in Cancer 26th

Transcription Factor-Centric Approach to Identify Non-recur...

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26th International Conference on Research in computational Molecular Biology, RECOMB 2022

作者： Zhao, Jingkang Martin, Vincentius Gordân, Raluca Center for Genomic and Computational Biology Duke University DurhamNC27708 United States Program in Computational Biology and Bioinformatics Duke University DurhamNC27708 United States Department of Computer Science Duke University DurhamNC27708 United States Department of Biostatistics and Bioinformatics Duke University DurhamNC27708 United States Department of Molecular Genetics and Microbiology Duke University DurhamNC27708 United States

ISBN: (纸本)9783031047480

Recent efforts to sequence the genomes of thousands of matched normal-tumor samples have led to the identification of millions of somatic mutations, the majority of which are non-coding. Most of these mutations are believed to be passengers, but a small number of non-coding mutations could contribute to tumor initiation or progression, e.g. by leading to dysregulation of gene expression. Efforts to identify putative regulatory drivers rely primarily on information about the recurrence of mutations across tumor samples. However, in regulatory regions of the genome, individual mutations are rarely seen in more than one donor. Instead of using recurrence information, here we present a method to prioritize putative regulatory driver mutations based on the magnitude of their effects on transcription factor-DNA binding. For each gene, we integrate the effects of mutations across all its regulatory regions, and we ask whether these effects are larger than expected by chance, given the mutation spectra observed in regulatory DNA in the cohort of interest. We applied our approach to analyze mutations in a liver cancer data set with ample somatic mutation and gene expression data available. By combining the effects of mutations across all regulatory regions of each gene, we identified dozens of genes whose regulation in tumor cells is likely to be significantly perturbed by non-coding mutations. Overall, our results show that focusing on the functional effects of non-coding mutations, rather than their recurrence, has the potential to prioritize putative regulatory drivers and the genes they dysregulate in tumor cells. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Transcription factors

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Quantum variational autoencoder utilizing regularized mixed-state latent representations

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Physical Review A 2025年第4期111卷 042416-042416页

作者： Gaoyuan Wang Jonathan Warrell Prashant S. Emani Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA NEC Laboratories America Princeton New Jersey 08540 USA Department of Computer Science Yale University New Haven Connecticut 06520 USA Department of Statistics & Data Science Yale University New Haven Connecticut 06520 USA Department of Biomedical Informatics & Data Science Yale University New Haven Connecticut 06520 USA

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no quantum model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To address this, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied to map both classical and quantum data to a lower-dimensional space, while effectively reconstructing much of the original state from it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full data density matrix and allow for a training objective defined on probabilistic mixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results indicate that ζ-QVAE consistently learns representations that better utilize the capacity of the latent space and exhibits similar or better performance compared with

关键词： Quantum computers

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Learning Multi-cellular Representations of Single-Cell Transcriptomics Data Enables Characterization of Patient-Level Disease States 29th

Learning Multi-cellular Representations of Single-Cell Tran...

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29th International Conference on Research in computational Molecular Biology, RECOMB 2025

作者： Liu, Tianyu De Brouwer, Edward Kuo, Tony Diamant, Nathaniel Missarova, Alsu Wang, Hanchen Hao, Minsheng Bravo, Hector Corrada Scalia, Gabriele Regev, Aviv Heimberg, Graham Research and Early Development Genentech South San FranciscoCA94080 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Roche Informatics F. Hoffmann-La Roche Ltd. Mississauga Canada Department of Computer Science Stanford University Palo AltoCA94035 United States

ISBN: (纸本)9783031902512

Single-cell RNA-seq (scRNA-seq) has become a prominent tool for studying human biology and disease. The availability of massive scRNA-seq datasets and advanced machine learning techniques has recently driven the development of single-cell foundation models that provide informative and versatile cell representations based on expression profiles. However, to understand disease states, we need to consider entire tissue ecosystems, simultaneously considering many different interacting cells. Here, we tackle this challenge by generating patient-level representations derived from multi-cellular expression context measured with scRNA-seq of tissues. We develop PaSCient, a novel model that employs a multi-level representation learning paradigm and provides importance scores at the individual cell and gene levels for fine-grained analysis across multiple cell types and gene programs characteristic of a given disease. We apply PaSCient to learn a disease model across a large-scale scRNA-seq atlas of 12.5 million cells from over 2,700 patients. Comprehensive and rigorous benchmarking demonstrates the superiority of PaSCient in disease classification and its multiple downstream applications, including dimensionality reduction, gene/cell type prioritization, and patient subgroup discovery. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Biomolecules

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Interannual variability and climatic sensitivity of global wildfire activity

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Advances in Climate Change Research 2021年第5期12卷 686-695页

作者： Rongyun TANG Jiafu MAO Mingzhou JIN Anping CHEN Yan YU Xiaoying SHI Yulong ZHANG Forrest M.HOFFMAN Min XU Yaoping WANG Institute for a Secure and Sustainable Environment University of TennesseeKnoxville37996USA Environmental Sciences Division and Climate Change Science Institute Oak Ridge National LaboratoryOak Ridge37830USA Department of Biology and Graduate Degree Program in Ecology Colorado State UniversityFort Collins80523USA Atmospheric and Oceanic Sciences Program Princeton UniversityPrinceton08544USA Computational Sciences and Engineering Division and Climate Change Science Institute Oak Ridge National LaboratoryOak Ridge37830USA

Understanding historical wildfire variations and their environmental driving mechanisms is key to predicting and mitigating wildfires. However, current knowledge of climatic responses and regional contributions to the interannual variability (IAV) of global burned area remains limited. Using recent satellite-derived wildfire products and simulations from version v1.0 of the land component of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM land model [ELM] v1) driven by three different climate forcings, we investigated the burned area IAV and its climatic sensitivity globally and across nine biomes from 1997 to 2018. We found that 1) the ELM simulations generally agreed with the satellite observations in terms of the burned area IAV magnitudes, regional contributions, and covariations with climate factors, confirming the robustness of the ELM to the usage of different climate forcing sources;2) tropical savannas, tropical forests, and semi-arid grasslands near deserts were primary contributors to the global burned area IAV, collectively accounting for 71.7%–99.7% of the global wildfire IAV estimated by both the satellite observations and ELM simulations;3) precipitation was a major fire suppressing factor and dominated the global and regional burned area IAVs, and temperature and shortwave solar radiation were mostly positively related with burned area IAVs;and 4) noticeable local discrepancies between the ELM and remote-sensing results occurred in semi-arid grasslands, croplands, boreal forests, and wetlands, likely caused by uncertainties in the current ELM fire scheme and the imperfectly derived satellite observations. Our findings revealed the spatiotemporal diversity of wildfire variations, regional contributions and climatic responses, and provided new metrics for wildfire modeling, facilitating the wildfire prediction and management.

关键词： Wildfire Burned area Climatic sensitivity E3SM land model Global fire emission database

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Social network analysis of manga: similarities to real-world social networks and trends over decades

arXiv

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arXiv 2023年

作者： Sugishita, Kashin Masuda, Naoki Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Center for Computational Social Science Kobe University Kobe657-8501 Japan

Manga, Japanese comics, has been popular on a global scale. Social networks among characters, which are often called character networks, may be a significant contributor to their popularity. We collected data from 162 popular manga that span over 70 years and analyzed their character networks. First, we found that many of static and temporal properties of the character networks are similar to those of real human social networks. Second, the character networks of most manga are protagonist-centered such that a single protagonist interacts with the majority of other characters. Third, the character networks for manga mainly targeting boys have shifted to denser and less protagonist-centered networks and with fewer characters over decades. Manga mainly targeting girls showed the opposite trend except for the downward trend in the number of characters. The present study, which relies on manga data sampled on an unprecedented scale, paves the way for further population studies of character networks and other aspects of comics. Copyright © 2023, The Authors. All rights reserved.

关键词： Population statistics

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Improving wind speed forecasting at Adama wind farm II in Ethiopia through deep learning algorithms

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Case Studies in Chemical and Environmental Engineering 2024年 9卷

作者： Chaka, Mesfin Diro Semie, Addisu Gezahegn Mekonnen, Yedilfana Setarge Geffe, Chernet Amente Kebede, Hailemichael Mersha, Yonas Anose, Fikru Abiko Benti, Natei Ermias Computational Data Science Program College of Natural and Computational Sciences Addis Ababa University P. O. Box 1176 Addis Ababa Ethiopia Center for Environmental Science College of Natural and Computational Sciences Addis Ababa University P. O. Box 1176 Addis Ababa Ethiopia Department of Physics College of Natural and Computational Sciences Addis Ababa University P.O. Box 1176 Addis Ababa Ethiopia Ethiopian Institute of Water Resources (EIWR) Addis Ababa University (AAU) Addis Ababa Ethiopia Department of Physics College of Natural and Computational Sciences Wolkite University Ethiopia

Wind power plays a critical role in supporting Ethiopia's electricity generation, particularly during dry seasons when hydropower availability diminishes. This contribution becomes even more crucial for sustaining industrial growth. However, accurately estimating wind energy remains challenging due to its random and highly variable nature. Deep learning models for time series prediction have been employed to address this issue, and this study evaluates the effectiveness of long short-term memory (LSTM) and gated recurrent unit (GRU) models in predicting wind speed at Adama Wind Farm II in Ethiopia. Model performance was assessed using R², RMSE, and MAE. GRU significantly outperformed LSTM across all metrics and forecasting horizons. For daily forecasting, GRU achieved an R² of 0.9727, exceeding LSTM's 0.9062 by 7.34 %, with corresponding RMSE values of 0.0005 and 0.0018, respectively. Similarly, GRU surpassed LSTM's performance for weekly and monthly forecasting. In weekly forecasting, GRU achieved an R² of 0.9969 compared to LSTM's 0.9930, with RMSE values of 0.0001 for both models. For monthly forecasting, GRU achieved an R² of 0.9330 compared to LSTM's 0.9303, with corresponding RMSE values of 0.0019 for both models. MAE values also followed the same pattern, with GRU consistently demonstrating lower values than LSTM across all forecasting horizons. These findings advance renewable energy and wind power forecasting research, highlighting the adaptability and versatility of LSTM and GRU models for capturing wind behavior. Decision-makers can use these findings to optimize wind power production, enhance grid operation efficiency, and promote sustainable and cost-effective wind energy. LSTM and GRU models have emerged as powerful tools for precise wind speed and wind power forecasting, which are essential for modern wind energy planning systems. They lay the foundation for a more environmentally friendly and sustainable en

关键词： Adama wind farm II Deep learning Ethiopia LSTM and GRU models Renewable energy Wind speed forecasting

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