ONR MURI: NexGeNetSci Universal Laws and Architectures John
Doyle John G Braun Professor Control and Dynamical Systems, BioEng,
ElecEng Caltech Third Year Review: October27, 2010 Plus a cast of
thousands
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Theory Data Analysis Numerical Experiments Lab Experiments
Field Exercises Real-World Operations First principles Rigorous
math Algorithms Proofs Correct statistics Only as good as
underlying data Simulation Synthetic, clean data Stylized
Controlled Clean, real-world data Semi- Controlled Messy,
real-world data Unpredictable After action reports in lieu of data
Doyle Universal Laws and Architectures layered multiscale
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Laws, laws, and architecture Conservation laws, constraints,
hard limits Important tradeoffs are between Control, computation,
communication, energy, materials, measurement Existing theory is
fragmented and incompatible Continuing progress on unifications
Power laws, data, models, high variability Architecture=
constraints that deconstrain Expand layering as optimization
Include human in loop and physical action/control Achieving hard
limits
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Triaged today Power laws, data, models, high variability
Estimating tails, MLE and WLS High variability in markets
Architecture Dynamics in layered architectures Case studies:
TCP/IP, cell, brain, wildfire ecology, Naming and addressing
details Beam forming details
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IEEE TRANS ON SYSTEMS, MAN, AND CYBERNETICS, JULY 2010 Alderson
and Doyle
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Each focuses on one dimension Important tradeoffs are across
these dimensions Need clean slate theories Progress is encouraging
(Old mysteries are also being resolved) wasteful fragile? slow ?
Thermodynamics (Carnot) Communications (Shannon) Control (Bode)
Computation (Turing) Standard system theories are severely
limited
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Robust Secure Scalable Evolvable Verifiable Maintainable
Designable Fragile Not Unverifiable Frozen Most dimensions are
robustness Collapse for visualization fragile
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wasteful fragile Conservation laws waste time waste resources
Important tradeoffs are across these dimensions Speed vs efficiency
vs robustness vs Robustness is most important for complexity
Collapse efficiency dimensions
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Conservation laws wasteful fragile ? ? ? ?
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Bad theory? ??? ? ? Bad architectures? wasteful fragile
gap?
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Sharpen hard bounds Hard limit Case studies wasteful fragile
Conservation laws
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Architecture Conservation laws Good architectures allow for
effective tradeoffs wasteful fragile Alternative systems with
shared architecture
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Sharpen hard bounds bad Find and fix bugs Complementary
approaches wasteful fragile Case studies
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bad Find and fix bugs wasteful fragile
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TCP IP Physical MAC Switch MAC Pt to Pt Diverse applications
Layered architectures
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App Applications Layered architecture Router Client Server
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App Applications Layered architecture Router Client Server 3.5
viewpoints on layered architecture: Operating systems Programming
languages Control and dynamical systems Operations research,
optimization Information theory
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Naming and addressing Names to locate objects 2.5 ways to
resolve a name 1.Exhaustive search, table lookup 2.Name gives hints
Extra is for indirection Address = name that involves
locations
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Operating systems OS allocates and shares diverse resources
among diverse applications Strict layering is crucial e.g. clearly
separate Application name space Logical (virtual) name/address
space Physical (name/) address space Name resolution within
applications Name/address translation across layers
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Benefits of stricter layering Black box effects of stricter
layering Portability of applications Security of physical address
space Robustness to application crashes Scalability of virtual/real
addressing Local variables and addresses Optimization/control by
duality?
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Problems with incomplete layering Black box benefits are lost
Global variables? @$%*&!^%@& Poor portability of
applications Insecurity of physical address space Fragile to
application crashes No scalability of virtual/real addressing
Limits optimization/control by duality?
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App kernel user In operating systems: Dont cross layers Direct
access to physical memory? In programming: No global variables
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App Applications Router
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App IPC Global and direct access to physical address! Robust?
Secure Scalable Verifiable Evolvable Maintainable Designable DNS IP
addresses interfaces not nodes
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Naming and addressing need to be resolved within layer
translated between layers not exposed outside of layer Related
issues DNS NATS Firewalls Multihoming Mobility Routing table size
Overlays
Embedded virtual actuator/ sensor Network cable Controller DIF
Physical plant Meta-layering of cyber-phys control
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Architecture Conservation laws Good architectures allow for
effective tradeoffs wasteful fragile Alternative systems with
shared architecture
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Embedded virtual actuator/ sensor DIF Collapsing the stack at
the edges Lib Physical plant Exploiting the physical Collapsing the
stack at the edges
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Architecture Good architectures allow for effective tradeoffs
wasteful fragile Exploiting the physical Collapsing the stack at
the edges
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Programmable Antenna Design Using Convex Optimization Lavaei,
Babakhani, Hajimiri, and Doyle Caltech Theory: Lavaei, Doyle
Experiment: Babakhani, Ali Hajimiri I Q
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Papers by Lavaei, Babakhani, Hajimiri, and Doyle, "Design of
Passively Controllable Smart Antennas for Wireless Sensor
Networks," Submitted to IEEE Transactions on Automatic Control.
"Solving Large-Scale Hybrid Circuit-Antenna Problems," To appear in
IEEE Transactions on Circuits and Systems I, 2010. "'Passively
Controllable Smart Antennas," to appear in IEEE Global
Communications Conference (GLOBECOM), Miami, Florida, 2010.
"Finding Globally Optimum Solutions in Antenna Optimization
Problems," in IEEE International Symposium on Antennas and
Propagation, Toronto, Canada, 2010. "Programmable Antenna Design
Using Convex Optimization," in Math. Theory of Networks and
Systems, Budapest,2010 (invited paper). "A Study of Near-Field
Direct Antenna Modulation Systems Using Convex Optimization," in
American Control Conference, Baltimore, 2010. "Solving Large-Scale
Linear Circuit Problems via Convex Optimization," in Proc. 48th
IEEE Conf on Dec. and Control, Shanghai, China, 2009. 33
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Summary of results 34 A passively controllable smart (PCS)
antenna that can be implemented as an integrated circuit and be
programmed in real time. Can be used for smart data transmission.
For the first time, excellent beam-forming patterns obtained with a
small-sized antenna. The programming of the PCS antenna overcomes
apparent intractability. Potentially completely changes what is
possible at wireless physical layer
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Sharpen hard bounds bad Find and fix bugs Complementary
approaches wasteful fragile Case studies
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Sharpen hard bounds wasteful fragile Case studies that achieve
bounds
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Theory plus biology case study Hard tradeoffs between Fragility
(disturbance rejection) Metabolic overhead Amount (of enzymes)
Complexity (of enzymes) Glycolytic oscillations Most ubiquitous and
studied circuit in science or engineering New insights and
experiments Resolves longstanding mysteries Biology component
funded by NIH and Army ICB
Fragility (standard control theory) rigorous,
first-principles.
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simple enzyme Enzyme amount complex enzyme Theorem z and p are
functions of enzyme complexity and amount standard biochemistry
models phenomenological first principles?
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10 10 0 1 0 1 Fragility Biological architectures achieve hard
limits and use complex enzymes and networks complex enzyme Enzyme
amount control of enzyme levels
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Fragility Metabolic overhead Architecture Conservation laws
Good architectures allow for effective tradeoffs Alternative
biocircuits with shared architecture
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Architecture Conservation laws Good architectures allow for
effective tradeoffs wasteful fragile Alternative systems with
shared architecture
Fragility hard limits simple Overhead, waste complex General
Rigorous First principle Domain specific Ad hoc Phenomenological
Plugging in domain details ?
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Fragility Overhead, waste General Rigorous First principle
Domain specific Ad hoc Phenomenological Plugging in domain details
? Fundamental multiscale physics Start classically Foundations,
origins of noise dissipation amplification
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IEEE TRANS ON AUTOMATIC CONTROL, to appear, FEBRUARY, 2011
Sandberg, Delvenne, and Doyle
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Layers in hardware So well-known as to be taken for granted
Digital abstraction and modularity Analog substrate is active and
lossy Microscopic world is lossless Reconcile these in a clear and
coherent way Exploit designable physical layer more
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+ step response V(t)V(t)
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+ 00.511.522.5 0 0.5 1 1.5 Time (sec) Amplitude dissipative,
lossy But the microscope world is lossless (energy is conserved).
Where does dissipation come from?
Theorem: Linear passive iff linear lossless approximation
Theorem: Linear active needs nonlinear lossless approximation
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System SenseEst. + - Physical implementation Back action Sensor
noise Consequences
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Back action System SenseEst. + - Sensor at temp T Short
interval ( 0, t ) Sensor noise Theorem
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System Sense Est. + - back-action error
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Sensor noise System Sense Est. + - error
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Back action System Sense Est. + - back-action
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System Sense Est. + - back-action error Theorem:
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System Sense Est. + - error Cold sensors are better and faster
(but not cheaper) back-action
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System Sense Est. + - back- action error larger t more data
smaller t less data
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System Sense Est. + - back- action error A transient and
far-from-equilibrium upgrade of statistical mechanics
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back- action error A transient and far-from-equilibrium upgrade
of statistical mechanics Estimation to control Efficiency of
devices, enzymes Classical to quantum
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10 -3 10 -2 10 10 0 0 1 2 3 mix unmix rank k Power laws
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SoCalFaults.pdf
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4 2lpnf-w 2 bigsur 12 3lpnf-nw Mix and unmixed fits well with
a=-.5 in body, Mix tail is deviating as expected 2 bigsur 4 2lpnf-w
12 3lpnf-nw These are the closest to our assumptions: Coastal
Chaparral large watersheds limited urban boundary 10 2 3 4 5 0 1 2
mechanistic model
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4 2lpnf-w 2 bigsur 12 3lpnf-nw mix Mix and unmixed fits well
with a=-.5 in body, Mix tail is deviating as expected 2 bigsur 4
2lpnf-w 12 3lpnf-nw These are the closest to our assumptions:
Coastal Chaparral large watersheds limited urban boundary 10 2 3 4
5 0 1 2
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4 2lpnf-w 2 bigsur 12 3lpnf-nw mix Mix and unmixed fits well
with a=-.5 in body, Mix tail is deviating as expected 2 bigsur 4
2lpnf-w 12 3lpnf-nw These are the closest to our assumptions:
Coastal Chaparral large watersheds limited urban boundary 10 2 3 4
5 0 1 2 model
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4 2lpnf-w 2 bigsur 12 3lpnf-nw Cutoffs are crucial Size of all
of CA hectares
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Pareto distribution with finite-scale effect MLE WLS WLS with
cutoff
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Triaged today Power laws, data, models, high variability
Estimating tails, MLE and WLS High variability in markets
Architecture Dynamics in layered architectures Case studies:
TCP/IP, cell, brain, wildfire ecology, Naming and addressing
details Beam forming details
Slide 81
Laws, laws, and architecture Conservation laws, constraints,
hard limits Important tradeoffs are between Computation, control,
communication, energy, materials, measurement Existing theory is
fragmented and incompatible Continuing progress on unifications
Power laws, data, models, high variability Architecture=
constraints that deconstrain Expand layering as optimization
Include human in loop and physical action/control Achieving hard
limits