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A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions

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Building Simulation 2022年第2期15卷 197-211页

作者： Cheng Fan Meiling Chen Rui Tang Jiayuan Wang Key Laboratory for Resilient Infrastructures of Coastal Cities(Shenzhen University) Ministry of EducationChina Sino-Australia Joint Research Center in BIM and Smart Construction Shenzhen UniversityShenzhenChina Building Technology&Urban Systems Division Lawrence Berkeley National LaboratoryUSA

Short-term building energy predictions serve as one of the fundamental tasks in building operation *** large numbers of studies have explored the value of various supervised machine learning techniques in energy predictions,few studies have addressed the potential data shortage problem in developing data-driven *** promising solution is data augmentation,which aims to enrich existing building data resources for reliable predictive *** study proposes a deep generative modeling-based data augmentation strategy for improving short-term building energy *** types of conditional variational autoencoders have been designed for synthetic energy data generation using fully connected and one-dimensional convolutional layers *** experiments have been designed to evaluate the value of data augmentation using actual measurements from 52 *** results indicate that conditional variational autoencoders are capable of generating high-quality synthetic data samples,which in turns helps to enhance the accuracy in short-term building energy *** average performance enhancement ratios in terms of CV-RMSE range between 12%and 18%.Practical guidelines have been obtained to ensure the validity and quality of synthetic building energy *** research outcomes are valuable for enhancing the robustness and reliability of data-driven models for smart building operation management.

关键词： building energy predictions data augmentation data-driven models generative modeling variational autoencoders

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AB-Gen:Antibody Library Design with generative Pre-trained Transformer and Deep Reinforcement Learning

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 1043-1053页

作者： Xiaopeng Xu Tiantian Xu Juexiao Zhou Xingyu Liao Ruochi Zhang Yu Wang Lu Zhang Xin Gao Computational Bioscience Research Center(CBRC) King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia State Key Laboratory of Structural Chemistry Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China University of Chinese Academy of Sciences Beijing 100049China Syneron Technology Guangzhou 510000China Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry Fuzhou 361005China

Antibody leads must fulfill multiple desirable properties to be clinical *** due to the low throughput in the experimental procedure,the need for such multiproperty optimization causes the bottleneck in preclinical antibody discovery and development,because addressing one issue usually causes *** developed a reinforcement learning(RL)method,named AB-Gen,for antibody library design using a generative pre-trained transformer(GPT)as the policy network of the RL *** showed that this model can learn the antibody space of heavy chain complementarity determining region 3(CDRH3)and generate sequences with similar property ***,when using human epidermal growth factor receptor-2(HER2)as the target,the agent model of AB-Gen was able to generate novel CDRH3 sequences that fulfill multi-property ***,509 generated sequences were able to pass all property filters,and three highly conserved residues were *** importance of these residues was further demonstrated by molecular dynamics simulations,consolidating that the agent model was capable of grasping important information in this complex optimization ***,the ABGen method is able to design novel antibody sequences with an improved success rate than the traditional propose-then-filter *** has the potential to be used in practical antibody design,thus empowering the antibody discovery and development *** source code of AB-Gen is freely available at Zenodo(https://***/10.5281/zenodo.7657016)and BioCode(https://***/biocode/tools/BT007341).

关键词： Protein design Transformer Reinforcement learning generative modeling Multi-objective optimization

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VAE-KRnet and its Applications to Variational Bayes

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Communications in Computational Physics 2022年第4期31卷 1049-1082页

作者： Xiaoliang Wan Shuangqing Wei Department of Mathematics Center for Computation and TechnologyLouisiana State UniversityBaton Rouge 70803USA Division of Electrical&Computer Engineering Louisiana State UniversityBaton Rouge 70803USA

In this work,we have proposed a generative model,called VAE-KRnet,for density estimation or approximation,which combines the canonical variational autoencoder(VAE)with our recently developed flow-based generativemodel,called *** is used as a dimension reduction technique to capture the latent space,and KRnet is used to model the distribution of the latent *** a linear model between the data and the latent variable,we show that VAE-KRnet can be more effective and robust than the canonical ***-KRnet can be used as a density model to approximate either data distribution or an arbitrary probability density function(PDF)known up to a ***-KRnet is flexible in terms of *** the number of dimensions is relatively small,KRnet can effectively approximate the distribution in terms of the original random *** high-dimensional cases,we may use VAE-KRnet to incorporate dimension *** important application of VAE-KRnet is the variational Bayes for the approximation of the posterior *** variational Bayes approaches are usually based on the minimization of the Kullback-Leibler(KL)divergence between the model and the *** highdimensional distributions,it is very challenging to construct an accurate densitymodel due to the curse of dimensionality,where extra assumptions are often introduced for *** instance,the classical mean-field approach assumes mutual independence between dimensions,which often yields an underestimated variance due to *** alleviate this issue,we include into the loss the maximization of the mutual information between the latent random variable and the original random variable,which helps keep more information from the region of low density such that the estimation of variance is *** experiments have been presented to demonstrate the effectiveness of our model.

关键词： Deep learning variational Bayes uncertainty quantification Bayesian inverse problems generative modeling

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