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Roadmap

A connected learning path toward AI for Process Systems Engineering.

Engineering77%
AI29%
Overall47%

Engineering

Foundation

  • Status: Completed. Learning progress: 100%.

  • Status: Completed. Learning progress: 100%.

  • Status: Completed. Learning progress: 100%.

  • Status: Completed. Learning progress: 100%.

  • Status: Completed. Learning progress: 100%.

PSE Core

  • Status: In progress. Learning progress: 50%.

  • Status: Completed. Learning progress: 100%.
    • Book
    • Course
    • Source

  • Status: Completed. Learning progress: 100%.
    • Book
    • Course
    • Source

  • Status: In progress. Learning progress: 50%.

  • Status: In progress. Learning progress: 50%.

  • Status: Completed. Learning progress: 100%.

  • Status: Planned. Learning progress: 0%.
    • Book
    • Course
    • Source

  • Status: In progress. Learning progress: 50%.

Engineering information branch

  • Status: Completed. Learning progress: 100%.
    • Book
    • Course
    • Source

  • Status: In progress. Learning progress: 50%.

AI

Foundation

  • Status: Completed. Learning progress: 100%.

  • Status: In progress. Learning progress: 50%.

  • Status: In progress. Learning progress: 50%.

  • Status: Planned. Learning progress: 0%.

Scientific / Engineering AI

  • Status: Planned. Learning progress: 0%.

  • Status: Planned. Learning progress: 0%.

  • Status: Planned. Learning progress: 0%.

  • Status: Planned. Learning progress: 0%.

AI Systems

  • Status: In progress. Learning progress: 50%.

  • Status: In progress. Learning progress: 50%.

  • Status: Planned. Learning progress: 0%.
    • Book
    • Course
    • Source

  • Status: Planned. Learning progress: 0%.
    • Book
    • Course
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  • Status: In progress. Learning progress: 50%.

Convergence capability

  • Status: In progress. Learning progress: 50%.
    • Book
    • Course
    • Source

Cross-track convergence

    • Process Modeling
    • Process Simulation
    • Hybrid Physics–Data Modeling

    Status: Planned. Learning progress: 0%.
    • Book
    • Course
    • Source
    • Process Optimization
    • Uncertainty & Decision Making
    • Uncertainty Quantification

    Status: Planned. Learning progress: 0%.
    • Book
    • Course
    • Source
    • PFD / P&ID
    • Engineering Data & Semantics
    • Knowledge & Graph Representation
    • Multimodal AI

    Status: Planned. Learning progress: 0%.
    • Book
    • Course
    • Source
  • Process Systems Engineering
  • Engineering AI

Status: Planned. Learning progress: 0%.
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  • Course
  • Source

Roadmap relationships

  • Material & Energy Balances leads toward Numerical Methods.
  • Material & Energy Balances leads toward Process Modeling.
  • Chemical Thermodynamics leads toward Process Modeling.
  • Transport Phenomena leads toward Process Modeling.
  • Reaction Engineering leads toward Process Modeling.
  • Separation Processes leads toward Process Simulation.
  • Numerical Methods leads toward Process Modeling.
  • Numerical Methods leads toward Process Simulation.
  • Numerical Methods leads toward Process Optimization.
  • Process Modeling leads toward Process Simulation.
  • Process Modeling leads toward Process Dynamics & Control.
  • Process Simulation leads toward Process Optimization.
  • Process Dynamics & Control leads toward Process Optimization.
  • Process Simulation leads toward Process Design & Synthesis.
  • Process Optimization leads toward Process Systems Engineering.
  • Process Design & Synthesis leads toward Process Systems Engineering.
  • Uncertainty & Decision Making leads toward Process Systems Engineering.
  • Process Design & Synthesis leads toward PFD / P&ID.
  • PFD / P&ID leads toward Engineering Data & Semantics.
  • Python & Scientific Computing leads toward Machine Learning.
  • Math for ML leads toward Machine Learning.
  • Machine Learning leads toward Deep Learning.
  • Machine Learning leads toward Surrogate Modeling.
  • Deep Learning leads toward Scientific ML.
  • Surrogate Modeling leads toward Scientific ML.
  • Scientific ML leads toward Hybrid Physics–Data Modeling.
  • Scientific ML leads toward Uncertainty Quantification.
  • Hybrid Physics–Data Modeling leads toward Engineering AI.
  • Uncertainty Quantification leads toward Engineering AI.
  • Evaluation & Reliability leads toward Engineering AI.
  • Knowledge & Graph Representation leads toward Engineering AI.
  • Multimodal AI leads toward Engineering AI.
  • Deep Learning leads toward Transformers & LLMs.
  • Transformers & LLMs leads toward Agents & Tool Use.
  • Agents & Tool Use leads toward Evaluation & Reliability.
  • Transformers & LLMs leads toward Knowledge & Graph Representation.
  • Transformers & LLMs leads toward Multimodal AI.
  • Process Modeling leads toward AI for Modeling & Simulation.
  • Process Simulation leads toward AI for Modeling & Simulation.
  • Hybrid Physics–Data Modeling leads toward AI for Modeling & Simulation.
  • Process Optimization leads toward AI for Optimization.
  • Uncertainty & Decision Making leads toward AI for Optimization.
  • Uncertainty Quantification leads toward AI for Optimization.
  • PFD / P&ID leads toward Engineering Drawing Automation.
  • Engineering Data & Semantics leads toward Engineering Drawing Automation.
  • Knowledge & Graph Representation leads toward Engineering Drawing Automation.
  • Multimodal AI leads toward Engineering Drawing Automation.
  • Process Systems Engineering leads toward AI for Process Systems Engineering.
  • Engineering AI leads toward AI for Process Systems Engineering.
  • AI for Modeling & Simulation leads toward AI for Process Systems Engineering.
  • AI for Optimization leads toward AI for Process Systems Engineering.
  • Engineering Drawing Automation leads toward AI for Process Systems Engineering.