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John Doyle 道陽 Jean-Lou Chameau Professor Control and Dynamical Systems, EE, & BioE tech 1 # Ca Universal laws and architectures: Theory and lessons from.

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Presentation on theme: "John Doyle 道陽 Jean-Lou Chameau Professor Control and Dynamical Systems, EE, & BioE tech 1 # Ca Universal laws and architectures: Theory and lessons from."— Presentation transcript:

1 John Doyle 道陽 Jean-Lou Chameau Professor Control and Dynamical Systems, EE, & BioE tech 1 # Ca Universal laws and architectures: Theory and lessons from brains, bugs, nets, grids, planes, docs, fire, bodies, fashion, earthquakes, turbulence, music, buildings, cities, art, running, throwing, Synesthesia, spacecraft, statistical mechanics

2 A minimal prologue

3 Diverse applications Aerospace Robotics Biology Fluids Physics Medicine Internet Smartgrid Ecology Neuroscience Tuesday Wednesday Thursday Monday

4 Diverse applicationsDiverse mathematics Aerospace Robotics Biology Fluids Physics Medicine Internet Smartgrid Ecology Neuroscience ODE/PDE Automata Analysis Optimization Prob&Stats Operator Th Diff Geom Alg Geom Cmp Cplxty Details are not serious

5 CDS Diverse applications (not just engineering) Diverse mathematics (not just “applied”) ODE/PDE Automata Analysis Optimization Prob&Stats Operator Th Diff Geom Alg Geom Cmp Cplxty “Universal laws and architectures” Aerospace Robotics Biology Fluids Physics Medicine Internet Smartgrid Ecology Neuroscience

6 CDS Diverse applications Domain experts have “core” interface to diverse math and tools Aerospace Robotics Biology Fluids Physics Medicine Internet Smartgrid Ecology Neuroscience

7 CDS Diverse mathematics Math (not just applied) has “core” interface to diverse application drivers ODE/PDE Automata Analysis Optimization Prob&Stats Operator Th Diff Geom Alg Geom Cmp Cplxty

8 CDS as architecture Architecture= “constraints that deconstrain” –Constraint = CDS core concepts and math –Deconstraint = connections between new applications and (also possibly new) math How to explain this? Laws: Universal constraints on achievable robust performance and efficiency Architectures: Universal organizational strategies to flexibly achieve what is possible Case studies: concrete and familiar examples to illustrate “universals” 8

9 Core protocols From Architecture slides, understanding the “OS” is difficult and essential But term “OS” seems to confuse Replace with “core protocols”? Highly conserved/constrained core (knot) Highly diverse/deconstrained edges What is “CDS” in this context? 9

10 Fast Slow Flexible Inflexible General Special Apps OS HW Horizontal App Transfer Horizontal Hardware Transfer

11 Fast Slow Flexible Inflexible General Special Apps OS HW Tradeoffs

12 Fast Slow Flexible Inflexible General Special Apps OS HW Tradeoffs = “laws?”

13 Slow Fast Flexible Inflexible General Special Apps OS HW Dig. Lump. Distrib. OS HW Dig. Lump. Distrib. Digital Lump. Distrib. Lumped Distrib. HGT DNA repair Mutation DNA replication Transcription Translation Metabolism Signal SenseMotor Prefrontal Fast Learn Reflex Evolve vision VOR Layered architectures

14 OS Horizontal App Transfer Horizontal Hardware Transfer Constrained Deconstrained Architecture = Constraints that deconstrain

15 Horizontal Transfer Constrained Deconstrained “Hourglass” Universal architectures

16 Constrained Deconstrained “Bowtie” Horizontal versus vertical is an arbitrary convention.

17 CDS Diverse applications (not just engineering) Diverse mathematics (not just “applied”) ODE/PDE Automata Analysis Optimization Prob&Stats Operator Th Diff Geom Alg Geom Cmp Cplxty “Universal laws and architectures” Aerospace Robotics Biology Fluids Physics Medicine Internet Smartgrid Ecology Neuroscience

18 “Pasteur’s quadrant?” (Donald Stokes) Typically poor choice of colors and font sizes

19 “Pasteur’s quadrant?” (Donald Stokes) Low High Edison Bohr Practical use Fundamental Understanding Low High Relative emphasis Use inspired basic research (Pasteur)

20 Low High Edison Pasteur Bohr Practical use Fundamental Understanding Low High “Pasteur’s quadrant?” Redrawn Relative emphasis Pasteur aimed for both

21 Apps CDS Math Practical use Fundamental Understanding

22 Later Deep, Universal Apps CDS Math Practical impact Specific Now CDS’ quadrant? Scope

23 Later Deep, Universal Apps CDS Math Practical impact Specific Now Fast Slow Flexible Inflexible General Special SW Apps OS HW Related but somewhat flipped Scope

24 Later Deep, Universal Apps Math Specific Now Circa 1975 Modern control theory Practical impact Scope

25 Later Deep, Universal Apps Math Specific Now Circa 1990 Complexity science Robust control theory Modern control theory Practical impact Scope

26 Later Deep, Universal Apps Math Specific Now 2014 CDS Complexity science Network science Never Nothing Practical impact Scope

27 Later Deep, Universal Specific Now Complexity science Network science Never Nothing Practical impact Scope Humans love this corner!

28 CMS Later Deep, Universal Apps Math Practical impact Specific Now Near future? CDS Complexity science Network science

29 CMS Later Deep, Universal Apps Math Practical impact Specific Now The dream? CDS Complexity science Network science

30 Horizontal Transfer Constrained Deconstrained Universal architectures Universal laws Fast Slow Flexible Inflexible General Special SW Apps OS HW

31 Slow Fast Flexible Inflexible General Special Apps OS HW Dig. Lump. Distrib. OS HW Dig. Lump. Distrib. Digital Lump. Distrib. Lumped Distrib. HGT DNA repair Mutation DNA replication Transcription Translation Metabolism Signal SenseMotor Prefrontal Fast Learn Reflex Evolve vision VOR Layered architectures

32 CDS Diverse applications “core” interface to minimal math and tools Aerospace Robotics Biology Fluids Physics Medicine Internet Smartgrid Ecology Neuroscience Morning Universal laws and architectures Undergrad math

33 CDS Diverse mathematics Networks, Distributed Cutting edge theory foundations ODE/PDE Automata Analysis Optimization Prob&Stats Operator Th Diff Geom Alg Geom Cmp Cplxty Afternoon

34 CMS Later Deep, Universal Apps Math Practical impact Specific Now The dream? CDS Complexity science Network science

35 Caveats Many ideas are classic “robust control”, but much of the organization (the architecture) is fairly new Motivating case studies also new Many rough edges Nobody is working on it quite like this We want to change that


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