AI-Driven Workflow for Automated Simulation Convergence Monitoring

Failed or divergent simulations consume expensive HPC resources without producing usable results. This workflow deploys monitoring agents that detect non-convergence early—often within the first 10-20% of runtime—and either applies corrective parameter adjustments or terminates the job. This directly converts wasted node-hours into productive throughput, protecting simulation budgets and accelerating project timelines.

Manual monitoring and restarting of simulations creates days of delay. By automating convergence checks and corrective actions, the workflow ensures that simulation queues complete reliably overnight. Engineers spend less time babysitting jobs and more time analyzing results, compressing design iteration cycles from weeks to days and enabling more exploratory parameter studies within the same timeframe.

Unstable runs often produce misleading or noisy data. The workflow's diagnostic agents use ML to classify failure modes (e.g., mesh distortion, timestep instability) and apply domain-tuned fixes, leading to more stable, physically meaningful results. This increases trust in simulation-led decisions for material selection, safety validation, and performance optimization, reducing downstream physical validation risk.

The architecture integrates directly with ANSYS, COMSOL, Abaqus, and open-source solvers via their APIs or job output parsing. An orchestration agent (built with LangGraph or Prefect) manages the state machine: launch job -> stream logs -> analyze metrics -> trigger adjustment. This creates a unified control plane for heterogeneous simulation environments, making HPC operations predictable and scalable.

By logging every intervention—reason, action, outcome—the workflow generates a rich dataset on simulation stability. This feeds into predictive models that forecast compute needs and flag high-risk simulations before they run. Finance and R&D leadership gain visibility into HPC utilization trends, enabling smarter capacity planning and budget allocation based on actual throughput, not just allocated hours.

Implementation starts with a monitoring-only pilot, logging divergence events without taking action. After validating the diagnostic logic, corrective actions are introduced in a gated manner: minor parameter adjustments auto-apply, while major resubmissions require engineer approval via Slack or email. This phased approach builds trust, ensures safety, and allows tuning the agent's decision thresholds based on real operational feedback.

A persistent agent that ingests live solver logs and intermediate results from ANSYS, COMSOL, or Abaqus APIs. It applies ML models trained on historical run data to detect divergence signatures—such as residual oscillation, mass imbalance, or sudden energy spikes—far earlier than static timeouts. This agent triggers parameter adjustment or graceful job suspension, converting a wasted 48-hour cluster run into a 2-hour diagnostic event.

Upon receiving an alert from the monitoring agent, this system executes a rule-based and reinforcement learning (RL) workflow. It queries a knowledge base of solver best practices and past successful adjustments for similar geometries and physics. The engine then dynamically rewrites the simulation input file (e.g., .inp, .cas) to modify timestep, relaxation factors, or mesh adaptation settings, and resubmits the job from the last stable iteration.

This component manages the lifecycle of simulation jobs on Slurm, PBS, or Kubernetes clusters. It interfaces with the scheduler to hold, modify, restart, or terminate jobs based on agent directives. It ensures adjusted jobs are prioritized correctly and maintains an audit trail of all interventions for cost attribution and process improvement. This layer is critical for integrating autonomous control into existing, high-stakes engineering compute environments.

Not all issues can be resolved autonomously. This gateway classifies alerts by severity and routes them via Slack, Teams, or email with contextual snapshots. For critical divergence or repeated adjustment failures, it creates a ticket in Jira or ServiceNow, attaching the relevant log snippets and suggested expert actions. This ensures human oversight is preserved for novel problems while filtering out routine noise.

A centralized database that stores the telemetry of every monitored simulation—inputs, divergence events, corrective actions, and final outcomes. This graph is continuously updated and used to retrain the monitoring ML models. It enables the system to learn from past interventions, improving its diagnostic accuracy and adjustment strategies over time, turning operational data into a competitive asset in simulation reliability.

Built using an agentic framework like LangGraph for orchestration, with lightweight containers for each specialized agent. It integrates via REST APIs and CLI wrappers with the simulation software stack (e.g., ANSYS Mechanical, Fluent) and the HPC scheduler. Deployment is typically phased, starting with a non-critical project queue to validate the monitoring logic and adjustment rules before full rollout across the R&D simulation portfolio.