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

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

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

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

IEEE TRANS ON SYSTEMS, MAN, AND CYBERNETICS, JULY 2010 Alderson and Doyle

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

Robust Secure Scalable Evolvable Verifiable Maintainable Designable … Fragile Not … Unverifiable Frozen … Most dimensions are robustness Collapse for visualization fragile

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

Conservation laws wasteful fragile ? ? ? ?

Bad theory?  ???  ? ? Bad architectures? wasteful fragile gap?

Sharpen hard bounds Hard limit Case studies wasteful fragile Conservation laws

Architecture “Conservation laws” Good architectures allow for effective tradeoffs wasteful fragile Alternative systems with shared architecture

Sharpen hard bounds  bad  Find and fix bugs Complementary approaches wasteful fragile Case studies

 bad  Find and fix bugs wasteful fragile

TCP IP Physical MAC Switch MAC Pt to Pt Diverse applications Layered architectures

App Applications Layered architecture Router Client Server

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

Naming and addressing Names to locate objects 2.5 ways to resolve a name 1.Exhaustive search, table lookup 2.Name gives hints Extra ½ is for indirection Address = name that involves locations

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

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?

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?

App kernel user In operating systems: Don’t cross layers Direct access to physical memory? In programming: No global variables

App Applications Router

App IPC Global and direct access to physical address! Robust? Secure Scalable Verifiable Evolvable Maintainable Designable … DNS IP addresses interfaces not nodes

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 Lib App DIF Networked/embedded/layered Lib Physical plant

Embedded virtual actuator/ sensor Network cable Controller DIF Physical plant Meta-layering of cyber-phys control

Architecture “Conservation laws” Good architectures allow for effective tradeoffs wasteful fragile Alternative systems with shared architecture

Embedded virtual actuator/ sensor DIF Collapsing the stack at the edges Lib Physical plant Exploiting the physical Collapsing the stack at the edges

Architecture Good architectures allow for effective tradeoffs wasteful fragile Exploiting the physical Collapsing the stack at the edges

Programmable Antenna Design Using Convex Optimization Lavaei, Babakhani, Hajimiri, and Doyle Caltech Theory: Lavaei, Doyle Experiment: Babakhani, Ali Hajimiri I Q

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, "'Passively Controllable Smart Antennas," to appear in IEEE Global Communications Conference (GLOBECOM), Miami, Florida, "Finding Globally Optimum Solutions in Antenna Optimization Problems," in IEEE International Symposium on Antennas and Propagation, Toronto, Canada, "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, "Solving Large-Scale Linear Circuit Problems via Convex Optimization," in Proc. 48th IEEE Conf on Dec. and Control, Shanghai, China,

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

Sharpen hard bounds  bad  Find and fix bugs Complementary approaches wasteful fragile Case studies

Sharpen hard bounds wasteful fragile Case studies that achieve bounds

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

simple enzyme Fragility Enzyme amount complex enzyme Theorem

Fragility (standard control theory) rigorous, first-principles.

simple enzyme Enzyme amount complex enzyme Theorem z and p are functions of enzyme complexity and amount standard biochemistry models phenomenological first principles?

Fragility Biological architectures achieve hard limits and use complex enzymes and networks complex enzyme Enzyme amount control of enzyme levels

Fragility Metabolic overhead Architecture “Conservation laws” Good architectures allow for effective tradeoffs Alternative biocircuits with shared architecture

Architecture “Conservation laws” Good architectures allow for effective tradeoffs wasteful fragile Alternative systems with shared architecture

Phenomenology 1.Incorporate domain specifics 2.First principles models

Fragility hard limits simple Overhead, waste complex General Rigorous First principle Domain specific Ad hoc Phenomenological Plugging in domain details ?

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

IEEE TRANS ON AUTOMATIC CONTROL, to appear, FEBRUARY, 2011 Sandberg, Delvenne, and Doyle

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

+ step response V(t)V(t)

Time (sec) Amplitude dissipative, lossy But the microscope world is lossless (energy is conserved). Where does dissipation come from?

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

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

step response Lossless Approximate T= Time (sec) Amplitude n=10

T=1 n= n=100

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

Time (sec) Amplitude n=10 step response Lossless Approximate

random initial conditions n= Response to Initial Conditions Time (sec) Amplitude

T=1 n=100 n= Response to Initial Conditions Time (sec) Amplitude

T= Dissipation Fluctuation Theorem: Fluctuation  Dissipation

Theorem: Linear passive iff linear lossless approximation Theorem: Linear active needs nonlinear lossless approximation

System SenseEst. + - “Physical” implementation Back action Sensor “noise” Consequences

Back action System SenseEst. + - Sensor at temp T Short interval ( 0, t ) Sensor “noise” Theorem

System Sense Est. + - back-action error

Sensor “noise” System Sense Est. + - error

Back action System Sense Est. + - back-action

System Sense Est. + - back-action error Theorem:

System Sense Est. + - error Cold sensors are better and faster (but not cheaper) back-action

System Sense Est. + - back- action error larger t more data smaller t less data

System Sense Est. + - back- action error A transient and far-from-equilibrium upgrade of statistical mechanics

back- action error A transient and far-from-equilibrium upgrade of statistical mechanics Estimation to control Efficiency of devices, enzymes Classical to quantum

mix unmix rank k Power laws

SoCalFaults.pdf

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 mechanistic model

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

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 model

4 2lpnf-w 2 bigsur 12 3lpnf-nw Cutoffs are crucial Size of all of CA hectares

Pareto distribution with finite-scale effect MLE  WLS  WLS with cutoff

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

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