Predictive Science Lab

Creating artificial intelligence (AI) technologies that accelerate the pace of engineering innovation.

We are an interdisciplinary research group at the School of Mechanical Engineering, Purdue University led by Prof. Ilias Bilionis.

MISSION
Our mission is to create artificial intelligence (AI) technologies that accelerate the pace of engineering innovation.

APPLICATIONS
Our applications span the range between purely technical (e.g., electric machines, high-performance materials) and sociotechnical (e.g., smart buildings, extra-terrestrial habitats).

PHILOSOPHY
Our research develops communication channels between theories and data. The communication protocol is based on probability theory (thought of as an extension of logic under uncertainty) with an additional layer of causality (expressed through physical laws and graphical models) and is realized using modern machine learning techniques based on deep neural networks.

FUNDING
We are extremely grateful for the support of our academic, government, and private sponsors:
Sponsors

Posts

News, thoughts, etc.

Making publication-ready figures for papers

This is a quick guide for making figures that are publication-ready using matplotlib. Note: PSL students must folow these directions to …
Last updated on Nov 25, 2019 2 min read

MSSP Paper Published: Identification of an Experimental Nonlinear Energy Sink Device Using the Unscented Kalman Filter

Last updated on Nov 25, 2019 1 min read

JCP Paper Accepted: Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

Last updated on Nov 21, 2019 1 min read

Meet the Team

Click here to see how past group members are doing.

Principal Investigator

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Ilias Bilionis

Associate Professor of Mechanical Engineering

Grad Students

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Alana Lund

Ph.D. Candidate

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Sharmila Karumuri

Ph.D. Student

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Alex Alberts

Ph.D. Student

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Vanessa Kwarteng

Ph.D. Student

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Andres Beltran

Ph.D. Student

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Atharva Hans

Ph.D. Student

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Nimish Awalgaonkar

Ph.D. Student

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Murali Krishnan Rajasekharan Pillai

Ph.D. Student

Papers

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Pratik Naik, Piyush Pandita, Soroush Aramideh, Ilias Bilionis, Arezoo M. Ardekani (2019). Bayesian model calibration and optimization of surfactant-polymer flooding. Computational Geosciences.

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Piyush Pandita, Ilias Bilionis, Jitesh Panchal (2019). Bayesian optimal design of experiments for inferring the statistical expectation of expensive black-box functions. Journal of Mechanical Design.

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Alana Lund, Shirley J. Dyke, Wei Song, Ilias Bilionis (2019). Global sensitivity analysis for the design of nonlinear identification experiments. Nonlinear Dynamics.

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Peña Francisco, Bilionis Ilias, Dyke Shirley (2019). Model selection and uncertainty quantification of seismic fragility functions. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering.

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Seungjae Lee, Jaewan Joe, Panagiota Karava, Ilias Bilionis, Athanasios Tzempelikos (2019). Implementation of a self-tuned HVAC controller to satisfy occupant thermal preferences and optimize energy use. Energy and Buildings.

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Research

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All Current Past NASA NSF DARPA Ford Facebook

Computing Contact Problems with Self-Conforming Hybrid Materials

Realize a novel hybrid material that self-conforms around an object of interest as a physical route for computing and reporting the …

Resilient Extraterrestrial Habitats

Developing smart autonomous deep space habitats that will adapt, absorb and rapidly recover from disruptions.

Using data science to discover materials with extreme properties.

Discovery of high-temperature, oxidation resistant, complex, concentrated alloys via data science driven multi-resolution experiments …

Physics-Informed LeaRnIng for Multiscale Systems (PILgRIMS)

Baking known physics into machine learning algorithms.

Sociotechnical Systems to Enable Smart and Connected Energy-Aware Residential Communities

Incentivizing subsidized communities to consume less energy.

Automated Decision Support to Address Community Resilience Challenges

Improving the resilience of the built environment through an automated vision-based system that generates probabilistic predictions.

Design of electric machines with manufacturing uncertainties

Development of computational tools for electric machine design including manufacturing uncertainties.

Efficient Algorithms for Ultra-fast Detection of Power System Contingencies in the Transient Regime.

Ultra-fast detection of faults in the power grid.

Causal inference on Bayesian graphical networks

Learn potential causal structures with quantified uncertainties from observational data.

A Theoretical Framework for Understanding Strategic Behavior in Systems Engineering

Understanding how incentive misalignment affects the design of complex systems.

Understanding Information Acquisition Decisions in Systems Design through Behavioral Experiments and Bayesian Analysis

Learning to replicate how engineers solve complex problems.

Human-centered Systems for Cyber-enabled Sustainable Buildings

Understanding thermal and visual comfort in high-performance buildings.

Teaching

This is a list of the courses Prof. Bilionis teaches on a regular basis. He has also delivered special short courses on research topics, e.g., uncertainty quantification, Gaussian process regression, global optimization of expensive functions, sequential design of simulations/experiments, which will be added to the site gradually.

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All Current Past Undergraduate Graduate

Purdue ME 270 – Basic Mechanics I (Statics)

Introduction forces, moments, and static equilibrium.

Purdue ME 274 – Basic Mechanics II (Dynamics)

Introduction to dynamics of rigid bodies

Introduction to Uncertainty Quantification

Quantifying uncertainties in physical systems.

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