The document discusses the evolution of engineering complexity, highlighting the transition from simple, predictable environments to complex, interactive systems. It covers concepts such as self-assembly, self-repair, self-recycling, and adaptive technologies within engineering subsystems. The text emphasizes new engineering paradigms that promote autonomy, adaptability, and the management of interdependent systems instead of relying on traditional rigid models.

1. Engineering Complexity Nicolas Demassieux © Nicolas Demassieux

2. Complexity of Interacting Systems Internet Security Hubble mirror ARIANE 5 first flight – Space shuttle © Nicolas Demassieux

3. Engineering Environment PAST Simple predictable environment Low interactions Low complexity Expensive Control Cheap Energy A priori validation Strict plans (“design”) FUTURE Complex environments Complex interactions Really High complexity Cheap Control Costly Energy In situ validation Indirect plans (“specification”) © Nicolas Demassieux

4. Life “know-how” CELL Self Assembly Self Repair Self Recycling DNA Robustness Replication Evolution ORGANISM Energy Supply Intelligence Adaptation ECOSYSTEMS Co-evolution Diversity Long term survival © Nicolas Demassieux

5. Engineering SUBSYSTEMS Self Assembly : Boot, Pilot channels , Molecular engineering, Quantum dots, Nanotubes, Shape memory alloys, ... Self Repair : Disk unfragmentation, Self check, Fault tolerance, ... Self Recycling : Garbage collectors, Self disassembly, biodegradable materials ... DESIGN Robustness : DIGITAL Replication : Software Objects (Inheritance)... Evolution : Monte Carlo, Genetic Algorithms, Artificial life... SYSTEMS Energy Supply : Intelligence : Neural networks, Agent technology, Cellular Automatas, AI... Adaptation : Self optimizing systems, Software radios, Adaptive techniques TECHNOSPHERE Co-evolution : the Internet, Radio ecosystems, service pyramid…. Diversity : Non convergence Long term survival : Non optimality © Nicolas Demassieux

6. One Example : Optimisation CONTINUOUS OPTIMISATION Energy landscape Local minima Gradient algorithms, simulated annealing BOOLEAN OPTIMISATION Boolean function : {0,1} n  {0,1} Problem : Synthesis using boolean gates with minimal cost …. a NP hard problem Solutions : Iterative heuristics, genetic programming, ... 0 1 0 1 1 0 1 0 A  B  C © Nicolas Demassieux

7. One Example : Optimisation (2) © Nicolas Demassieux COMPLEX MULTIPARAMETER SYSTEMS CONTROL Problem : “tweak the knobs of a system” to optimize its behavior in a given complex environment Solutions : genetic programming, optimal rate distorsion, self optimizing systemes, self awareness... DCT Q image reconstruite image source VLC mémoire d'image + - + + Q -1 DCT -1 VLC -1 mémoire d'image estimation mouvement Q -1 DCT -1 Buffer Buffer COMPLEX MULTIDIMENSIONAL “SHAPES” IDENTIFICATION Problem : Classification, learning Solutions : VQ, Neural network, fractal ...

8. New Engineering Culture PAST Models, black Boxes “ Hard Specifications” Optimization Engineered control Independent Systems Confidence FUTURE No more complete models Evolving specifications Overprovision Autonomous control Interdependent Systems Humility Engineering Optimized Systems Managing Technical Environments © Nicolas Demassieux