Welcome to Neural Input Modeling

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Neural Input Modeling is a new way to model and generate input processes using Generative Neural Networks

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