Preliminary, alphabetically ordered list of papers accepted for inclusion in the DX’25 proceedings as full papers.
The papers cover a range of topics within the field of diagnosis and prognosis, including:
* **LLM Applications:** Exploring the potential of Large Language Models for diagnostic concepts and fault detection.
* **Fault Diagnosis & Isolation:** Techniques for identifying and locating faults in systems like HVAC, control systems, and radiotherapy equipment, utilizing methods like dynamic slicing, spectrum-based fault localization, and data-driven approaches.
* **Prognosis & Remaining Useful Life:** Predicting future system behavior and estimating remaining useful life, including approaches using particle filters and spectral fault receptive fields.
* **Hybrid Systems & Machine Learning:** Utilizing one-shot learning and deep learning clustering for system identification and predictive maintenance.
* **Competition & Benchmarks:** Details about the DX 2025 competition and its associated benchmarks.
* **Model-Based Diagnosis:** Using qualitative simulation models and fusing temporal logic with probabilistic diagnosis.
The list includes 15 accepted papers with authors and titles provided. The conference will be held from September 22-24, 2025, in Nashville, Tennessee.
The papers cover a range of topics within the field of diagnosis and prognosis, including:
* **LLM Applications:** Exploring the potential of Large Language Models for diagnostic concepts and fault detection.
* **Fault Diagnosis & Isolation:** Techniques for identifying and locating faults in systems like HVAC, control systems, and radiotherapy equipment, utilizing methods like dynamic slicing, spectrum-based fault localization, and data-driven approaches.
* **Prognosis & Remaining Useful Life:** Predicting future system behavior and estimating remaining useful life, including approaches using particle filters and spectral fault receptive fields.
* **Hybrid Systems & Machine Learning:** Utilizing one-shot learning and deep learning clustering for system identification and predictive maintenance.
* **Competition & Benchmarks:** Details about the DX 2025 competition and its associated benchmarks.
* **Model-Based Diagnosis:** Using qualitative simulation models and fusing temporal logic with probabilistic diagnosis.
The list includes 15 accepted papers with authors and titles provided. The conference will be held from September 22-24, 2025, in Nashville, Tennessee.
Brian Douglas, a control systems engineer based in Seattle, has created various resources over the years and invites visitors to explore them.
- Linear (PID, lead-lag, full-state feedback)
Nonlinear (gain scheduling, backstepping, sliding mode)
Optimal & Predictive Control
LQR, MPC, Hamilton-Jacobi-Bellman
- Intelligent & Multi-Agent Control
Reinforcement learning, genetic algorithms, swarm control
- Robust & Adaptive Control
- H-infinity, mu-synthesis, active disturbance rejection, model reference adaptive control
- Planning & Estimation
- Path planning (RRT, A*), Kalman filters, sensor fusion
- Modeling, Simulation & System Analysis
- State-space, transfer functions, stability analysis, Nyquist, Lyapunov
- Linear (PID, lead-lag, full-state feedback)
Nonlinear (gain scheduling, backstepping, sliding mode)
Optimal & Predictive Control
LQR, MPC, Hamilton-Jacobi-Bellman
- Intelligent & Multi-Agent Control
Reinforcement learning, genetic algorithms, swarm control
- Robust & Adaptive Control
- H-infinity, mu-synthesis, active disturbance rejection, model reference adaptive control
- Planning & Estimation
- Path planning (RRT, A*), Kalman filters, sensor fusion
- Modeling, Simulation & System Analysis
- State-space, transfer functions, stability analysis, Nyquist, Lyapunov
This article explores the application of reinforcement learning (RL) to Partial Differential Equations (PDEs), highlighting the complexity and challenges involved in controlling systems described by PDEs compared to Ordinary Differential Equations (ODEs). It discusses various approaches, including genetic programming and neural network-based methods, and presents experimental results on controlling PDE systems like the diffusion equation and Kuramoto–Sivashinsky equation. The author emphasizes the potential of machine learning to improve understanding and control of PDE systems, which have wide-ranging applications in fields like fluid dynamics, thermodynamics, and engineering.
