1. 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

2. 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

3. 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

4. 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

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

6. 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

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

9. 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

10. Conservation laws wasteful fragile ? ? ? ?

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

12. Sharpen hard bounds Hard limit Case studies wasteful fragile Conservation laws

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

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

15.  bad  Find and fix bugs wasteful fragile

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

17. App Applications Layered architecture Router Client Server

18. 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

19. 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

20. 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

21. 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?

22. 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?

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

24. App Applications Router

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

26. 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 …

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

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

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

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

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

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

33. 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

34. 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

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

36. Sharpen hard bounds wasteful fragile Case studies that achieve bounds

37. 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

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

39. simple enzyme Fragility Enzyme amount complex enzyme Theorem

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

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

42. 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

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

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

45. Phenomenology 1.Incorporate domain specifics 2.First principles models

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

47. 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

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

49. 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

50. + step response V(t)V(t)

51. + 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?

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

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

54. 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

55. 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

56. 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

57. 00.511.522.5 -1.5 -0.5 0 0.5 1 1.5 Time (sec) Amplitude n=10 step response Lossless Approximate

58. 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

59. 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

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

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

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

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

64. System Sense Est. + - back-action error

65. Sensor “noise” System Sense Est. + - error

66. Back action System Sense Est. + - back-action

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

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

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

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

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

72. 10 -3 10 -2 10 10 0 0 1 2 3 mix unmix rank k Power laws

74. SoCalFaults.pdf

75. 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

76. 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

77. 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

78. 4 2lpnf-w 2 bigsur 12 3lpnf-nw Cutoffs are crucial Size of all of CA hectares

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

80. 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

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