Understand before you compute.
I build models to inform decisions in energy and medicine, helping an engineer size a system, an operator anticipate production, a physicist interpret a dose, and tools that make them usable. I start from the problem: what is known about the system, the data available and the precision really needed. I use an explicit model when it fits, complete it with learning when a relation is missing, and keep extra complexity only when its benefit is demonstrated. AI is fully part of the toolbox; evaluation and responsibility stay human.
I am a physicist and Research Director at Mines Paris–PSL, within the Centre Observation, Impacts, Énergie (O.I.E.), and Visiting Professor at the University of Kragujevac, Serbia. My work connects solar and photovoltaic forecasting, time series and frugal AI. My background also includes medical physics.
Can we know how predictable the sky is? When does a simple model beat a complex one? Which forecast errors matter in practice? Here I explain the results behind these questions, with the papers and code that support them.
The domain changes; the method does not. The route is chosen by the problem and the decision to inform, not by the tool; extra complexity is kept only when its benefit is demonstrated.
When the link is known, compute it with an explicit model, calibrated and validated on data; learning is added only for a demonstrated benefit.
Compute, don't learn: radiobiological dose equivalence · Simulate what you cannot yet measure: road tunnels · How forecastable is the sun?
When the model falls short, complete it: keep the model, learn only what is missing.
Thyroid cancer and radioiodine: model first · Better persistence, without training a neural network
When the useful relations are not known well enough, evaluate what the data can teach, with only as much complexity as pays.
Seven signals, one ELM · Satellite forecasting: one cell or the whole field? · Frugal models for solar forecasting (in French)
Language models help people use validated tools; the calculation, the checking and the responsibility stay with validated codes and people.
Judge against a reference, publish code, data and limits, make research findable and reusable.
Questioning how we do research: influence, integrity, excellence · AI in research: the AI Act, a lab charter and a toolbox · When RMSE hides a difference · Beyond pointwise errors: the γ-index · Academic visibility: why publishing isn't enough anymore · Research excellence in the age of AI: from SEO to GEO (in French)
How forecastable is the sun?
Separating cloud-driven variability from the daily solar cycle: the sCV and forecastability indices, evaluated at 68 stations.
A better persistence forecast, without training a model
BLEND uses the recurring statistical structure of energy signals to improve a simple reference forecast.
When a displaced forecast is still useful
The γ-index evaluates spatial, temporal and intensity agreement within explicit tolerances.
Also explore multi-output energy forecasting with ELM and NICE: measuring improvement over persistence.
Watch: Research Excellence in 2026
My talk on making research discoverable, verifiable and useful. Talk summary, references and preprint.
Solar forecasting: my course at Mines Paris–PSL
The UE14 course connects solar variability, time-series models, evaluation and frugal forecasting.
Full academic CV · Research projects · Google Scholar
Cyril Voyant is a physicist and Research Director at Mines Paris – PSL (Centre O.I.E.). Combining medical physics in radiotherapy (since 2003) and energy-forecasting research (PhD in 2011), he works on solar and energy forecasting, forecast evaluation, frugal AI and language-model interfaces to scientific codes. Background, research, projects, recognition and contact.