2017 Simulation Workshop

The 2017 I-Sim Research Workshop 2017 was held on July 31-August 2, 2017, University of Durham, UK.
The program organizers are Peter J. Haas (IBM Almaden Research Center, San Jose, USA) and Georgios Theodoropoulos (Durham University, UK).

Location: University of Durham, UK

Dates: July 31, - August 2, 2017

Theme: Towards an Ecosystem of Simulation Models and Data

Data is becoming a ubiquitous resource via, e.g., social networks, sensors, and the Internet of Things; at the same time, simulation models are increasingly complex, detailed, and large-scale. In this new world, the simulation community faces major challenges and opportunities both in exploiting available real-world data and in extracting insights from the massive data sets produced from large-scale simulation experiments. The focus of the 2017 Workshop is to promote fruitful interactions between the data science and simulation communities. An non-exhaustive list of topics is below:

• Data assimilation into simulation models (e.g., via Kalman or particle filtering)
• Data farming and experimental design for large-scale simulation experiments
• Data-driven simulations
• Calibration of complex (e.g., agent-based), data-hungry simulations
• Use of database management systems within simulation platforms
• Provenance management for large-scale simulation experiments
• Pushing simulation into database platforms
• Metamodeling, machine-learning and visual-analytic techniques for understanding massive simulation output
• Sharing data: Collaborative modeling and simulation



A collaborative simulation modelling continuum: Rise of Data and Machines

David Bell, Brunel University London

Project Switch
Philip Bowtel, P&G

Simulation and Direct Experimentation in the Tech Sector
Peter Frazier, Cornell University

Discrete Event Optimization via Simulation in the Era of Big Data
John Fowler, Arizona State University 

Top-down Statistical Modeling and Big Data
Peter Glynn, Stanford University

Ranking and Selection with Covariates
Jeff Hong, City University of Hong Kong

Big Simulation + Big Data: Numerically Conservative Parallel Query Processing for CFD Applications
Bill Howe, University of Washington

Optimization-based Uncertainty Quantification in Stochastic Simulation
Henry Lam, University of Michigan

Is it easier to optimize than to estimate in the presence of input model risk?
Barry Nelson, Northwestern University

It’s not just about Models and Data: Context is a Catalyst for Insight
Mary Roth, IBM

Data farming: methods for the present, opportunities for the future
Susan Sanchez, Naval Postgraduate School 

The end of Scientific Reasoning?
Peter Sloot, Universiteit van Amsterdam

A Dynamic Data-Driven Adaptive Multi-Scale Simulation (DDDAMS) for Planning and Control
Young-Jun Son, University of Arizona

Knowledge Discovery in Manufacturing Simulations
Steffen Strassburger, Ilmenau University of Technology

Data assimilation for agent-based models: the mathematical and computational challenges
Jonathan Ward, University of Leeds

Data assimilation for bilinear dynamical systems
Sergiy Zhuk, IBM


Dynamic Metamodeling of Simulated Systems

Russell Barton, Xinyi Wu

Exploration and exploitation in a non stationary environment
Andria Ellina, Christine Currie, Christophe Mues

Real-Time Data-Driven Redistribution in a Public Bike-Share System
Cynthiaann Bryant, Ben Morgenroth1, Felisa Vazquez-Abad and Michael Fu

Probabilistic Databases for Simulation: SimSQL
Peter Haas, Chris Jermaine

Simulation Galleries – Combining Experiment Design and Visual Analytic
Tom Warnke, Lars Roesicke, Hans-Jörg Schulz and Adelinde Uhrmacher