HAM for BIGCYBHybrid Analysis and Modeling for Big Data Cybernetics
Adil RASHEED, Department of Engineering CyberneticsNorwegian University of Science and Technology
Trondheim, Norway
Seminar on Big Data CyberneticsNov 27 2019 Scandic Nidelven
Modelling in the digitalized world
• Generalizable• Seen vs unseen problems
• Trustworthy• Interpretable, explainable, honest
• Computationally efficient• Realtime modelling
• Dynamically adapting and evolving• Continuously learning new physics
Physics based modeling
Ocean-Met Interactions
Physics based modeling Generalizable Trustworthy Computationally inefficient Static
Data-driven modeling
90.4% 3.5% 1.35% 1.22%
Data-driven modeling Computationally efficient Dynamically adaptable and evolving
Automatic Feature Detection
TREE
TREE
HOUSE
90.4% 3.5% 1.35% 1.22%
Data-driven modeling Computational efficiency Dynamic Adaptation Non-generalizable Blackbox
Physics based modeling Generalizable Trustworthy Computationally inefficient Static
Data-driven modeling Non-generalizable
Blackbox Computationally efficient
Dynamically adapting and evolving
Hybrid Analysis and Modeling
Generalizable Trustworthy Computationally efficient Dynamically adapting and evolving
HAM as an enabler for Big Data Cybernetics
Big Data Cybernetics: A new paradigm in steering the world with big data characterized by high volume, high velocity, highvariety and high veracity
HAM at work
Mass conservationMomentum conservation
Energy conservationHumidity conservation
Mass conservationMomentum conservation
Step 1: Physics based modeling
Unexplained physics
Observation
High fidelity physics based simulation
Snapshots using LIDARS and RADARS
Physics based model
Generalizable Trustworthy Computationally
inefficient Dynamically static and
inaccurate
Step 2: Interpretable data-driven approach
1 1
| | | | || | | | |
| | | | || | | | |
tnX ω ω ω
=
X ΣV *
Φ
Σ V*
E== +m n×
Φr r×
n n×
m m× m n× m r×
r n×
m n×
99.95% variance captured by the first eight modes
0.05% error
Observation data
Orthonormal basis
Galerkin projection Generalizable Trustworthy Computationally
inefficient Dynamically static and
inaccurate
Unknown physicsPhysics based model
Reduced Order Model
Projected
Generalizable Trustworthy Computationally efficient Dynamically static and inaccurate
Computationally expensive and inaccurate
Unknown physics
Computationally inexpensive but accurateProjected unknown
physics
1 1
| | | | || | | | |
| | | | || | | | |
tnX ω ω ω
=
TX V= ΦΣ
Blackbox Deep LearningWith in-built sanity check
Potentially Noise
Modeledresidual
Generalizable Trustworthy Computationally efficient Dynamically adapting and evolving
Unknown physics
Projected unknownphysics
1 1
| | | | || | | | |
| | | | || | | | |
tnX ω ω ω
=
TX V= ΦΣ
Interpretable Symbolic regressionPotentially
Noise Physics discovery
Generalizable Trustworthy Computationally efficient Dynamically adapting and evolving
Realtime model update
Conclusions
• HAM for BIGCYBGeneralizableTrustworthyComputationally efficientDynamically evolving and accurate
• Relevant for Digital TwinsInternet of ThingsSafety critical autonomous systems
Publications on HAM Vaddireddy H, Rasheed A, Staples AE, San O, Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensors, Download
Pawar S, Ahmed SE, San O and Rasheed A, Data-driven recovery of hidden physics in reduced order modeling of fluid flows, Download
Pawar S, Ahmed SE, San O and Rasheed A, An evolve-then-correct reduced order model for hidden fluid dynamics, Download
Pawar S, San O, Rasheed A and Vedula P, A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence, Download
Robinson H, Rasheed A, San O, Dissecting Deep Neural Networks, Download
Rasheed A, San O and Kvamsdal T, Digital Twin: Values, Challenges and Enablers, Download
Pawar S, Rahman Sk. M, San O, Rasheed A and Navon IM, MEMROM: Memory EMbedded Reduced Order Modeling of non-ergodic flows, To appear in Physics of Fluids, Download
Rahman Sk. M, Pawar S, San O, Rasheed A, Iliescu T, A non-intrusive reduced order modeling framework for quasi-geostrophic turbulence, To appear in the Physical Review EDownload
Pawar S, Rahman Sk. M, Vaddireddy H, San O, Rasheed A, and Vedula P, A deep learning enabler for non-intrusive reduced order modeling of fluid flows, Physics of Fluids, 31, 085101,2019 Download
Maulik R, San O, Rasheed A, Vedula P, Data-driven deconvolution for large eddy simulations of Kraichnan turbulence, Physics of Fluids, 30, 125109 (2018)Download
Fonn E, Brummelen H, Kvamsdal T and Rasheed A, Finite Element Divergence-Conforming POD-Galerkin formulation for the development of novel reduced order models, Computer Methods in Applied Mechanics and Engineering, Volume 346, 1 April 2019, Pages 486-512, Download Preprint
Maulik R, San O, Rasheed A, Vedula P, Sub-grid modelling for two-dimensional turbulence using neural networks, Journal of Fluid Mechanics, 858, 122-144, 2019 Download
Rahman SM, San O, and Rasheed A, A hybrid approach for model order reduction of barotropic quasi-geostrophic turbulence, Fluids, 3(4), 86, 2018
Rahman SM, Rasheed A and San O, A hybrid analytic framework for accelerating incompressible flow solvers, Fluids 2018, 3(3), 50
https://arxiv.org/pdf/1911.05254.pdfhttps://arxiv.org/pdf/1910.13909.pdfhttps://arxiv.org/pdf/1911.02049.pdfhttps://arxiv.org/pdf/1910.07132.pdfhttps://arxiv.org/pdf/1910.03879.pdfhttps://arxiv.org/pdf/1910.01719.pdfhttps://arxiv.org/pdf/1910.07649.pdfhttps://arxiv.org/pdf/1906.11617.pdfhttps://arxiv.org/pdf/1907.04945.pdfhttps://arxiv.org/pdf/1812.02211.pdfhttps://arxiv.org/abs/1807.11866https://arxiv.org/pdf/1808.02983.pdf
HAM for BIGCYBModelling in the digitalized worldSlide Number 3Slide Number 4Slide Number 5Slide Number 6Slide Number 7Slide Number 8Slide Number 9Slide Number 10Slide Number 11Slide Number 12Slide Number 13Slide Number 14Slide Number 15Slide Number 16Step 1: Physics based modelingStep 2: Interpretable data-driven approachSlide Number 19Slide Number 20Slide Number 21Slide Number 22Slide Number 23ConclusionsPublications on HAM
