Physical Systems Modeling · Probabilistic Reasoning · Scientific Software
My work spans power systems, weather and environmental modeling, forecasting, engineering simulation and production software. I am drawn to problems where physical models, imperfect measurements, uncertain operating conditions and computational limits all have to be brought together to support an engineering decision.
Models that combine physical constraints with uncertainty in operating conditions, component states, parameters and measurements.
Probabilistic inference · uncertainty quantification · risk analysis · Bayesian reasoning · scenario analysis
Modeling and software for transmission systems, dynamic line ratings, grid operations, renewable energy, forecasting and reliability.
DLR/AAR · transmission · weather–grid interaction · contingency risk · EMS integration
Physics-based modeling of interconnected systems: thermal, fluid, environmental and energy.
Conservation laws · thermal systems · CFD · hydrology · physical simulation
Turning mathematical and engineering models into software that engineers and operators can actually use.
Python · Java/Spring · TypeScript/Angular · MATLAB · PostgreSQL/PostGIS · AWS · Docker
Featured project
TERRA is an experimental computational framework I have been developing to explore how physical simulation and probabilistic inference can be combined for engineering systems operating under uncertainty.
Traditional analysis asks
What happens under this scenario?
TERRA explores
Given what we know now, what states of the system are plausible, what could fail, how likely are those outcomes, and what observation would help us understand the system better?
TERRA represents uncertain physical systems as graphical probabilistic models coupled to physical constraints and engineering solvers, so the conservation laws and the uncertainty live in one structure.
What can fail, how likely is it, and what are the consequences?
Which component, parameter or measurement best explains what we observe?
What is probably happening where we cannot measure directly?
Which uncertainties contribute most to system risk?
What should we measure next to reduce uncertainty most?
How would risk change under a different operating condition or intervention?
Probabilistic contingency analysis and physical risk assessment under uncertainty in weather, equipment availability, operating state, topology, ratings and measurements.
Probabilistic reasoning over interconnected power, cooling, thermal, compute and sensor systems.
Each study is runnable, with its numbers read from committed results.
Choosing the measurement that settles a diagnosis
A chilled-water loop serving about 860 kW of server heat looks far less efficient than it should, and twelve explanations — real faults and lying meters alike — could account for it. On the first five noisy readings the true cause, a return-temperature sensor reading low, ranked only third at about 11 %. TERRA recommended one specific follow-up check, an independent load heat audit; after it the true cause ranked first at about 57 %, a jump of roughly 46 points from a single measurement.
Demonstrates: evidence conditioning · Bayesian diagnosis · sensor uncertainty · experiment design
Finding measurement problems that threshold rules miss
Four scenarios were built in which one channel is corrupted by too little to trip its alarm, against a truth whose physics deliberately differs from the model used to recover it. TERRA ranked the corrupted channel first in 4 of 4 scenarios, against 2 of 4 for a peer-comparison baseline and 0 of 4 for alarm thresholds. The reconstructed replacement value beat the bad reading in every case, cutting the error by 26 % to 85 %.
Demonstrates: sensor diagnosis · latent-state inference · virtual sensing
Combinatorial uncertainty in transmission systems
Probabilistic contingency analysis on standard power-system test networks, examining how graphical structure, physical separators, probability-ordered enumeration and selective physical simulation affect computational cost and the accuracy of the inference.
Demonstrates: power flow · N−k contingencies · rare-event risk · graphical factorization · scalable simulation
A general text states the method; five domain editions teach the same method with that domain's own examples and case studies. All are free to read, and every number in them is generated from a committed result.
| Probabilistic Modeling of Boundary-Exchanging Physical Systems the general text | PDF, 405 pp |
|---|---|
| Probabilistic Modeling of Power Systems transmission and dispatch | PDF, 445 pp |
| Probabilistic Modeling of Water Distribution Networks | PDF, 386 pp |
| Probabilistic Modeling of District Heating and Cooling Networks | PDF, 369 pp |
| Probabilistic Modeling of Data Centers cooling, capacity and diagnosis | PDF, 390 pp |
| Probabilistic Modeling of Battery Energy Storage Systems preview, Parts I–III | PDF, 227 pp |
Working drafts, revised as the underlying studies land.
Engineering software and modeling for electric transmission, centered on weather-informed dynamic line ratings and grid operations.
Hydrologic and environmental modeling, flood forecasting, emergency-response software, geospatial analysis and engineering applications.
Electric-load and solar-power forecasting models, data pipelines and operational forecasting systems.
Solar irradiance forecasting, satellite image processing, radiation heat transfer, numerical modeling and scientific computing.
| Physical modeling | Power systems, thermal systems, hydrology, CFD, heat transfer, weather |
|---|---|
| Probabilistic modeling | Bayesian inference, graphical models, uncertainty quantification, Monte Carlo, probabilistic risk |
| Scientific computing | Python, MATLAB, C/C++, Fortran |
| Software engineering | Java, Spring, REST APIs, TypeScript, Angular |
| Data | PostgreSQL, PostGIS, time series, geospatial data |
| Infrastructure | AWS, Docker, Linux, Tomcat |
| Energy | Transmission, DLR/AAR, weather integration, forecasting, EMS |
PMP · iSAQB CPSA-F · CompTIA Security+ · ISO 27001 Lead Implementer · EC-Council DevSecOps
I am an engineer and software developer interested in computational problems at the boundary between physical modeling, probability and decision-making.
My career has moved across weather forecasting, hydrology, renewable-energy forecasting, transmission systems and engineering software. The common thread has been building models and software that turn incomplete physical information into useful engineering decisions.
More recently I have been exploring probabilistic physical-system reasoning through TERRA, particularly for electric grids and data-center infrastructure.
I enjoy problems that require moving between equations, simulation, data, software architecture and the engineering question that ultimately needs to be answered.
Questions about the method, corrections to the books, or work you think this fits — all welcome.