Graphics Processing Unit Technology

Jamshed, Shamoon

doi:10.1016/b978-0-12-801567-4.00008-8

Cited by 2 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, the simulator ABM at each wave during history matching does cost so much computing resource. It may be of use to bring in parallel computation, such as graphics processing unit technology [ 18 , 37 , 38 ] to accelerate the procedure. For example, each input of ABM needs to execute

times at each wave during history matching.…”

Section: Discussionmentioning

confidence: 99%

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

Cheng

Zhang

2017

IJMS

View full text Add to dashboard Cite

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency.

show abstract

times at each wave during history matching.…”

Section: Discussionmentioning

confidence: 99%

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

Cheng

Zhang

2017

IJMS

View full text Add to dashboard Cite

show abstract

“…Transformers take as input the whole sentence rather than a single word and generate different vectors for the same word depending on the context. The spaCy Transformer architectures run on GPU 39 for training and use the RoBERTa-base 40 model for the English pipelines. The configurations that include all settings and hyperparameters used for the training are reported in the Supporting Information.…”

Section: ■ Methodologymentioning

confidence: 99%

LIBAC: An Annotated Corpus for Automated “Reading” of the Lithium-Ion Battery Research Literature

et al. 2023

View full text Add to dashboard Cite

The lithium-ion battery (LIB) research literature has increased very rapidly of late. While this is an immense source of valuable knowledge and facts for the community, these are also partly "buried" in the literature. To truly make the most possible use of the information available and automate "reading", special tools are required. Named entity recognition (NER) is one such tool, which uses supervised machine learning for information extraction. To enable efficient NER, however, a large and highquality annotated corpus is crucial. Here, we report on our generated, semi-automatically annotated lithium-ion battery annotated corpus, "LIBAC", for 28 different entities of LIBs, which was used for training and evaluating Tok2vec and Transformer-based models, resulting in high general accuracies for these with F 1 -scores of 81 and 83%, respectively. LIBAC itself was created from 6985 paragraphs randomly chosen from ca. 11,000 LIB research papers and contains 73,300 annotated spans (627,428 tokens). This is the prime stepping-stone needed to develop a large-scale information extraction system designed for the LIB research literature.

show abstract

Graphics Processing Unit Technology

Cited by 2 publications

References 11 publications

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

LIBAC: An Annotated Corpus for Automated “Reading” of the Lithium-Ion Battery Research Literature

Contact Info

Product

Resources

About