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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

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Embedded virtual actuator/ sensor Network cable Controller Lib App DIF Networked/embedded/layered Lib Physical plant

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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

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Fragility (disturbance rejection) Metabolic overhead Amount (of enzymes) Complexity (of enzymes) Fragility hard limit simple enzyme Enzyme amount complex enzyme

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simple enzyme Fragility Enzyme amount complex enzyme Theorem

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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

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Phenomenology 1.Incorporate domain specifics 2.First principles models

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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?

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+ step response V(t)V(t) Lossless Approximate + step response dissipative, lossy

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+ step response V(t)V(t) Lossless Approximate Lossless Approximate

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step response Lossless Approximate T=1 00.511.522.5 -1.5 -0.5 0 0.5 1 1.5 Time (sec) Amplitude n=10

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T=1 n=10 00.20.40.60.81 -1.5 -0.5 0 0.5 1 1.5 00.20.40.60.81 -1.5 -0.5 0 0.5 1 1.5 n=100

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00.20.40.60.81 0 0.5 1 1.5 Time (sec) + step response V(t)V(t) Lossless Approximate T=1n=10 n=4

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00.511.522.5 -1.5 -0.5 0 0.5 1 1.5 Time (sec) Amplitude n=10 step response Lossless Approximate

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random initial conditions n=10 00.20.40.60.81 -2 -1.5 -0.5 0 0.5 1 1.5 Response to Initial Conditions Time (sec) Amplitude

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T=1 n=100 n=10 00.20.40.60.81 -2 -1.5 -0.5 0 0.5 1 1.5 Response to Initial Conditions Time (sec) Amplitude

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T=1 00.20.40.60.81 -1.5 -0.5 0 0.5 1 1.5 Dissipation Fluctuation Theorem: Fluctuation Dissipation

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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

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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