MechAI · Adam Krysztopa
Where the physicalworld meetsartificial intelligence.
PhD physicist and ex–GE Aerospace engineer, now an AI/ML team lead. I bridge complex physical systems and production-grade AI — predictive maintenance, digital twins, forecasting, and RAG / agentic systems for industrial environments.
- Heat Transfer
- GE Aerospace
- Predictive Maint.
- RUL & twins
- Forecasting
- time series
- Production AI
- RAG & agents
An engineer who learned to teach machines.
I'm a PhD physicist (physics of semiconductors) with an engineering background from GE Aerospace, where I led heat-transfer and secondary-flow design on the GE Catalyst, ATP, and CF6 programs — environments where a wrong assumption melts hardware, not a unit test. That respect for physical limits never left me.
Today I bridge complex physical systems and production-grade AI: predictive maintenance, digital twins, time-series forecasting, and production-ready RAG and agentic systems — turning domain expertise into scalable, explainable solutions for industrial environments at STX Next.
MechAI is my own practice for engineering-aware analytics — Oil & Gas reliability, Remaining Useful Life, digital twins, and forecastability triage. I'm the author of dependence-forecastability, an open-source toolkit for telling whether a time series is even worth forecasting before the expensive modeling begins.
Operating principles
- Physics firstConstraints are features, not obstacles.
- Ship the loopA model in production beats a notebook PB.
- Explainable by designIf an engineer can't trust it, it won't run.
- Measure the real costLatency, energy, and failure modes count.
From a thermal boundary to a working model — one circuit.
- IN01
Heat Transfer
Thermal systems and secondary-flow design at GE Aerospace — Catalyst, ATP, CF6. Where physical limits are non-negotiable.
- Thermal
- Secondary Flow
- GE
- x²02
Forecasting
Time-series and probabilistic forecasting — plus forecastability triage to know what's learnable before modeling begins.
- Time Series
- Probabilistic
- Triage
- ∇03
Predictive Maintenance
RUL, reliability, and digital twins for industrial assets — interpretable models operators actually trust.
- RUL
- Digital Twins
- Reliability
- AI04
Production AI
Elastic RAG and agentic systems — explainable, observable, and built to ship in regulated environments.
- RAG
- Agents
- LLMOps
Tools I've built for forecasting, reliability, and applied AI.
Writing from the seam between physics and machine learning.
- May 28, 2026Is It Even Forecastable? Triage Before You ModelMost forecasting projects start with model selection. They should start with a cheaper question — does this series contain exploitable structure at the horizon you care about?#Forecastability#TimeSeries#OpenSource2 min→
- May 12, 2026A Digital Twin Is Not a Fancy DashboardIn manufacturing, most 'digital twins' are real-time monitoring dressed up with 3D visuals. A real twin answers a different question: what happens before you change anything?#DigitalTwins#PredictiveMaintenance#Manufacturing2 min→
- Apr 15, 2026Remaining Useful Life: The Model Is the Easy PartRUL estimation for industrial assets looks like a regression problem. The hard parts are the label, the cost of being wrong, and whether an operator will trust the number.#PredictiveMaintenance#RUL#Reliability2 min→
- Mar 2, 2026From Heat Sinks to Backprop: An Engineer's Path into AIThe transition from mechanical engineering to AI isn't a pivot away from physics — it's physics gaining a new instrument.#Career#Engineering#Reflection1 min→
Have a problem where physics and AI collide?
Predictive maintenance, digital twins, time-series forecasting, or production RAG and agentic systems — or just figuring out whether ML is even the right tool — let's talk.