Midhun Xavier

Midhun Xavier

AI Research Engineer @ Aerobase | Neural Combinatorial Optimization & Reinforcement Learning

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Posts

Exploring the Potential Impact of NFTs on Industrial Control Systems: Opportunities and Risks

2 minute read

Published:

NFTs, or non-fungible tokens, have taken the world by storm in recent years. These digital assets, which use blockchain technology to verify ownership and uniqueness, have been used to sell everything from art to sports memorabilia. But what about the impact of NFTs on industrial control systems (ICS)? In this blog post, we’ll explore how NFTs could potentially affect the world of ICS.

publications

Plant model generator from digital twin for purpose of formal verification

Published in 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2021

Recommended citation: M. Xavier, J. Håkansson, S. Patil, V. Vyatkin. "Plant model generator from digital twin for purpose of formal verification." 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2021.

Process mining in industrial control systems

Published in 2022 IEEE 20th International Conference on Industrial Informatics (INDIN), 2022

Recommended citation: M. Xavier, V. Dubinin, S. Patil, V. Vyatkin. "Process mining in industrial control systems." 2022 IEEE 20th International Conference on Industrial Informatics (INDIN), 2022.

Formal verification of observers supervising a cyber-physical system implemented using IEC 61499

Published in 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), Helsinki, Finland, 2023

Recommended citation: P. Ovsiannikova, E. Le Priol, V. Perret, P. Jhunjhunwala, M. Xavier, V. Vyatkin. "Formal verification of observers supervising a cyber-physical system implemented using IEC 61499." 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), Helsinki, Finland, 2023.

Developing a Test Suite for Evaluating IEC 61499 Application Portability

Published in 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), Helsinki, Finland, 2023

Recommended citation: M. Xavier, T. Liakh, S. Patil, V. Vyatkin. "Developing a Test Suite for Evaluating IEC 61499 Application Portability." 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), Helsinki, Finland, 2023.

Formal modelling, analysis, and synthesis of modular industrial systems inspired by Net Condition/Event Systems

Published in 44th International Conference on Application and Theory of Petri Nets and Concurrency (PETRI NETS 2023), Lisbon, Portugal, 2023

Recommended citation: M. Xavier, S. Patil, V. Dubinin, V. Vyatkin. "Formal modelling, analysis, and synthesis of modular industrial systems inspired by Net Condition/Event Systems." 44th International Conference on Application and Theory of Petri Nets and Concurrency (PETRI NETS 2023), Lisbon, Portugal, 2023.

DeLMS: A Decentralized Learning Management System using Ethereum Smart Contracts and IPFS

Published in 2023 IEEE 21st International Conference on Industrial Informatics (INDIN), Lemgo, Germany, 2023

Recommended citation: M. Xavier, P. Sobha, S. Patil, V. Vyatkin. "DeLMS: A Decentralized Learning Management System using Ethereum Smart Contracts and IPFS." 2023 IEEE 21st International Conference on Industrial Informatics (INDIN), Lemgo, Germany, 2023.

Generating Portable Test Cases for IEC 61499 FBs from Interface Behaviour Specifications

Published in 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), Sinaia, Romania, 2023

Recommended citation: B. Wiesmayr, M. Xavier, S. Patil, A. Zoitl, V. Vyatkin. "Generating Portable Test Cases for IEC 61499 FBs from Interface Behaviour Specifications." 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), Sinaia, Romania, 2023.

Probabilistic Model Checking for IEC 61499: A Manufacturing Application

Published in 2024 IEEE International Conference on Industrial Technology (ICIT), 2024

Recommended citation: I. Faqrizal, T. Liakh, M. Xavier, G. Salán, V. Vyatkin. "Probabilistic Model Checking for IEC 61499: A Manufacturing Application." 2024 IEEE International Conference on Industrial Technology (ICIT), 2024.

teaching

Generalization and Scaling of Neural Solvers

Generalization & Scaling, LTU, 2025

A key challenge in Neural Combinatorial Optimization is generalizing to instances that are larger or drawn from different distributions than those seen during training. I investigate curriculum learning, data augmentation, and inference-time search strategies to improve out-of-distribution robustness, and I benchmark learned solvers against exact and metaheuristic baselines (OR-Tools, LKH, Gurobi) on solution quality, optimality gap, and runtime.

Learning Improvement Heuristics and Neural Local Search

Learned Heuristics, LTU, 2025

Beyond constructing solutions in a single pass, I research learned improvement operators that iteratively refine an existing solution. This includes neural local search, guided and large-neighborhood search, and hybrid schemes that combine learned policies with classical operations-research techniques to reach lower optimality gaps within a fixed compute budget.

Best-of-K Objectives: Optimizing for Pass@K and Max@K

Objectives & Evaluation, LTU, 2025

At inference time, neural solvers typically sample multiple candidate solutions and keep the best one. I am interested in training directly for these best-of-K settings by optimizing Pass@K and Max@K objectives, so that the training signal matches how models are actually deployed. This work explores objective design, variance reduction, and the trade-off between solution diversity and per-sample quality.

Reinforcement Learning and Policy Optimization for Combinatorial Problems

Policy Optimization, LTU, 2025

I study how modern policy-optimization algorithms can train neural solvers end-to-end without labelled optimal solutions. This includes policy-gradient and actor–critic methods, REINFORCE with greedy and rollout baselines, and recent algorithms such as GRPO, PKPO, RSPO, and PPO. My goal is to make training more stable, sample-efficient, and better aligned with the true optimization objective of the underlying combinatorial problem.

Neural Combinatorial Optimization with Attention Models

Neural Combinatorial Optimization, LTU, 2025

My core research interest is Neural Combinatorial Optimization (NCO): training deep neural networks to solve classic combinatorial optimization problems such as the Travelling Salesman Problem (TSP), Capacitated Vehicle Routing (CVRP), scheduling, and assignment. I focus on attention-based encoder–decoder architectures, Pointer Networks, and graph neural networks that learn to construct high-quality solutions directly from data, complementing or replacing hand-crafted heuristics.

Physics-Informed Reinforcement Learning for Defect Minimization in Additive Manufacturing

Applied RL (Aerobase), Aerobase, 2025

I develop reinforcement-learning methods that minimize defects — such as porosity, cracking, residual stress, and distortion — in parts produced by Directed Energy Deposition (DED), Laser Powder Bed Fusion (LPBF), and Wire Arc Additive Manufacturing (WAAM). The RL agents learn from multiphysics simulation feedback, including thermal and mechanical finite element (FEM) analysis, microstructure analysis, and melt-pool computational fluid dynamics (CFD) simulations, closing the loop between process parameters, deposition-path planning, and predicted part quality.

Combinatorial Optimization for Additive Manufacturing Toolpath and Robot Path Generation

Applied NCO (Aerobase), Aerobase, 2025

At Aerobase, I apply neural combinatorial optimization to robot path generation and toolpath planning across metal additive-manufacturing processes — Directed Energy Deposition (DED), Laser Powder Bed Fusion (LPBF), and Wire Arc Additive Manufacturing (WAAM). I formulate deposition- and scan-path sequencing as a combinatorial optimization problem and train attention-based neural solvers to generate paths that reduce travel, balance thermal load, and improve build quality, throughput, and material efficiency.