What is Neural Input Modeling?
Neural Input Modeling (NIM) is a generative-neural-network framework that exploits modern data-rich environments to automatically capture simulation input distributions and then generate samples from them. Experiments show that our prototype architecture NIM-VL, which uses a novel variational-autoencoder architecture with LSTM components, can accurately, and with no prior knowledge, automatically capture a range of complex stochastic processes and efficiently generate sample paths. NIM can help overcome one of the key barriers to simulation for non-experts.
WSC 2020 Talk
WSC 2019 Poster
People
- Graudate Students
- Faculty
Publications
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NIM: Modeling and generation of simulation inputs via generative neural networks
Wang Cen, Emily Herbert, Peter Haas
Paper @ Winter Simulation Conference 2020
Runner-up, Best Theoretical Contributed Paper -
Generative Neural Networks for Simulation Input Modeling (Extended Abstract and Poster)
Wang Cen, Emily Herbert, Peter Haas
Paper @ Winter Simulation Conference 2019 -
NIM: Generative Neural Networks for Modeling and Generation of Simulation Inputs (Short Paper)
Emily Herbert, Wang Cen, Peter Haas
Paper @ 2019 Summer Simulation Conference