Simulation-mesh optimization
A 21-trial search found a smaller mesh, then a separate solver pass checked flow and pressure against the fine reference.
Read the case studyOptimization for engineering and research
Looptimum helps teams select parameters for simulations, model training, and experiments where each evaluation requires substantial time or resources. The software uses previous results to guide the search, with evaluations and records kept in your own environment.
Optimization workflow
Define the adjustable parameters, operating limits, and objective. Looptimum proposes a configuration for your existing workflow to evaluate, then incorporates the result into the search.
- 01
Suggest
Looptimum selects the next candidate from your parameter bounds, using the results collected so far.
- 02
Evaluate
Your script, cluster, solver, or lab workflow runs the trial in its existing environment.
- 03
Ingest
Record the score or failure in local files, then repeat. The saved history lets you review decisions and resume interrupted work.
Campaign evidence examples
GPT training-parameter search
Ten evaluations tested four training controls. The selected model had lower held-out loss and fewer parameters than the fixed baseline.
Read the case studyApplications
Suitable projects have measurable outcomes, defined parameter ranges, and evaluations that consume significant compute, equipment, or staff time.
Simulation and engineering
Tune mesh controls, solver tolerances, or calibration parameters when each run takes substantial compute or analyst time.
Infrastructure tuning
Adjust concurrency, retries, memory limits, thread counts, or cache lifetimes against measured cost and latency.
Machine learning
Search training settings, batch sizes, or evaluation thresholds when experiments are slow and failed runs are costly.
Operational process tuning
Improve throughput, quality, or cost in lab workflows, ETL pipelines, and production processes while respecting operating limits.
Research and feasibility
The Looptimum Advanced Research Collective (LARC) works on early technical projects to assess system architecture, computing requirements, and experiment design before implementation.
Work may include bottleneck analysis, feasibility models, and a plan for initial experiments.
Read about LARCPilot assessment
We can review your evaluation workflow, optimization objective, and resource budget to assess a pilot.
David Y.
Developer
David is the founder and lead developer of Looptimum. His work spans computational hemodynamics, medical-device research, Bayesian optimization, and machine learning, including language-model development. He builds research software for simulation, statistical analysis, model calibration, and experiments. At Looptimum, he focuses on making costly evaluations more useful while keeping the underlying science and engineering sound.
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