Modeling and Simulation Tools for Industrial and Societal Research Applications:
Digital Twins, and Genome-based Machine-learning

Summary:

• Team simulates many processes, develops custom simulation codes for each

• Initial motivation: 

  • 3D laser printer

  • Open loop: no sensors on the laser itself

  • Huge thermal gradient on the object being cut with the laser

  • Laser cutting followed by milling and drilling using additional tool

  • Managing this thing requires complex multi-physics, multi-scale simulation

  • Coupled simulation:

    • Maxwell solver of the energy deposition

    • Fluid flow of the melted material

    • Heat conduction

    • Many unknown parameters of the robot position, environment and material being cut

• Domains: (they’ve implemented these themselves)

  • 3D Printing robots

  • Complex mixtures

  • Lasers

  • Solid processing

  • Next-gen drones and multi-aerial vehicles/swarms

  • Pandemic/COVID

  • Irradiation of surfaces to kill pathogens

  • Contact tracing (agent-based)

  • Explosions and inverse forensics of explosions

  • Power grid

  • Ballistics

  • Data Centers

  • Solar farms

  • Vaccine design

  • Complex fires

• Optimization framework:

  • Digital twin model of the device

  • Control loop

    • Measures state of system

    • Computes error between model and sensor measurements

    • Adjusts model state

    • Take next best action based on current model state and goal for device

• Emphasis is on the optimization:

  • Describe system using a “genomic” representation

  • Use genetic algorithms to tune configuration

  • Optimize by trying many random genomes (random strings)

  • Mutation, crossover operation to create next generation of genomes

  • Techniques:

    • Strong gene breeding: mate strong candidates

    • Weak gene elimination: remove poorly performing strings

    • Adaptively re-focus search at better options

  • Good for non-convex, non-smooth optimization spaces

  • Hierarchical search:

    • Have hierarchy of models with different performance/accuracy tradeoffs

    • Constrain parameter space using man runs of cheap model

    • Estimate model error to know whether we’re likely off

    • Given regions that look promising and/or where error is high, run more expensive models

  • Hierarchical 

• Novel approach: replace mesh-based codes with voxel-based codes for continuous systems
  
  

  • No need for meshing

  • Easy interface for implementing ODE/PDE logic

  • Simple way to change cost/precision tradeoff of the code

• Agent-based models is a good choice for discrete systems

• Big picture: combine digital twins with ML