TY - GEN
T1 - Complicacy-guided parameter space sampling for knowledge discovery with limited simulation budgets
AU - Chen, Xilun
AU - Mathesen, Logan
AU - Pedrielli, Giulia
AU - Candan, K. Selçuk
N1 - Funding Information:
Research is supported by NSF#1318788 “Data Management for Real-Time Data Driven Epidemic Spread Simulations”, NSF#1827757 “Data-Driven Services for High Performance and Sustainable Buildings”, NSF#1909555 “pCAR: Discovering and Leveraging Plausibly Causal (p-causal) Relationships to Understand Complex Dynamic Systems”, NSF#1610282 “DataStorm: A Data Enabled System for End-to-End Disaster Planning and Response”, NSF#1633381 “BIGDATA: Discovering Context-Sensitive Impact in Complex Systems”, and “FourCmodeling”: EU-H2020 Marie Sklodowska-Curie grant agreement No 690817.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Knowledge discovery and decision making through data-and model-driven computer simulation ensembles are increasingly critical in many application domains. However, these simulation ensembles are expensive to obtain. Consequently, given a relatively small simulation budget, one needs to identify a sparse ensemble that includes the most informative simulations to help the effective exploration of the space of input parameters. In this paper, we propose a complicacy-guided parameter space sampling (CPSS) for knowledge discovery with limited simulation budgets, which relies on a novel complicacy-driven guidance mechanism to rank candidate models and a novel rank-stability based parameter space partitioning strategy to identify simulation instances to execute. The advantage of the proposed approach is that, unlike purely fit-based approaches, it avoids extensive simulations in difficult-to-fit regions of the parameter space, if the region can be explained with a much simpler model, requiring fewer simulation samples, even if with a slightly lower fit.
AB - Knowledge discovery and decision making through data-and model-driven computer simulation ensembles are increasingly critical in many application domains. However, these simulation ensembles are expensive to obtain. Consequently, given a relatively small simulation budget, one needs to identify a sparse ensemble that includes the most informative simulations to help the effective exploration of the space of input parameters. In this paper, we propose a complicacy-guided parameter space sampling (CPSS) for knowledge discovery with limited simulation budgets, which relies on a novel complicacy-driven guidance mechanism to rank candidate models and a novel rank-stability based parameter space partitioning strategy to identify simulation instances to execute. The advantage of the proposed approach is that, unlike purely fit-based approaches, it avoids extensive simulations in difficult-to-fit regions of the parameter space, if the region can be explained with a much simpler model, requiring fewer simulation samples, even if with a slightly lower fit.
KW - Complicacy guided sampling
KW - Knowledge discovery
KW - Parameter space sampling
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=85078094129&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078094129&partnerID=8YFLogxK
U2 - 10.1109/ICBK.2019.00015
DO - 10.1109/ICBK.2019.00015
M3 - Conference contribution
AN - SCOPUS:85078094129
T3 - Proceedings - 10th IEEE International Conference on Big Knowledge, ICBK 2019
SP - 50
EP - 57
BT - Proceedings - 10th IEEE International Conference on Big Knowledge, ICBK 2019
A2 - Gao, Yunjun
A2 - Moller, Ralf
A2 - Wu, Xindong
A2 - Kotagiri, Ramamohanarao
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Big Knowledge, ICBK 2019
Y2 - 10 November 2019 through 11 November 2019
ER -