Computational Models of the Horse
In human medicine, computational models routinely answer questions that once required invasive studies — simulated blood flow guides cardiac care, virtual airways inform surgery. EquiPhysics is working toward the same future for horses: mathematical and computer models of equine circulation, respiration, and movement that could eventually let us study disease, soundness, and recovery in silico, reducing the need for experiments involving living horses.
Circulation. Computational fluid-dynamics models of pulsatile flow in the equine aorta, exploring how flow and wall stress vary with anatomy and exercise intensity.
Respiration. Models of airflow in the equine upper airway, where dynamic collapse is a serious and common health problem.
Rhythms. Heart, breath, and stride as coupled oscillators — the mathematics of how a horse’s rhythms coordinate, within one body and between horse and human.
The Horses at the Heart of It
EquiPhysics still begins at the barn. The program horses — Duque, Perseo, Mermaid, Jackson, and their herdmates — live at Rancho del Nido, and our team works with them nearly every day. Their care comes first: measurements are brief and noninvasive by design — lightweight wireless motion sensors (the same technology inside a smartwatch) worn for a few minutes of ordinary exercise, heart-rate straps of the kind riders use daily, and video — all under university-approved animal-care (IACUC) protocols at an inspected facility. What we learn with them anchors the models, fills the classroom datasets, and keeps the mathematics honest.
Movement: gait and asymmetry. Every horse’s movement is individual, and many — off-the-track Thoroughbreds especially — carry distinctive asymmetries. We measure them: how long each hoof stays on the ground, how each limb swings, how patterns hold or change across sessions, speeds, and directions. Perseo’s left front lingers about 47 milliseconds longer than his right, in both directions on the circle. How long Mermaid’s hind hooves stay on the ground varies more than twice as much as it does for her front — a pattern we’ve observed in no other horse. Differences like these add up to a movement fingerprint — enough that a computer can tell one horse from another by motion alone — and following them over months lets us watch recovery and retraining unfold in the data.
Rhythms: heart and breath. With the same light touch, we record heart rate and heart-rate variability (HR/HRV) and breathing rate and its variability (BR/BRV) — windows into how a horse’s autonomic nervous system responds and recovers. We’ve measured how horses’ heart rhythms respond to music across dozens of sessions, explored how breathing couples to stride across gaits, and begun asking how rhythms pass between horse and human. These are the rhythms our coupled-oscillator models are built to explain.
Throughout, we measure, compare, and model — we don’t prescribe. When a measurement raises a question about an individual horse, we bring it to the people who know that horse: veterinarians, farriers, trainers, bodyworkers. When our data showed one gelding’s right front lifting off a fraction of a second later than its pair, the answer came from his veterinary team and his farrier, working from the data together with what they could see and feel.
A sensor notices a 47-millisecond difference no eye can see. An experienced horsewoman notices context no sensor records. One of the questions we find most interesting is how these two kinds of knowing fit together — where they agree, where they differ, and what each catches that the other misses. Neither replaces the other, and we wouldn’t want it to.
We share analysis notebooks, teaching datasets, and complete classroom labs openly — a classroom anywhere can teach with the real movement and breath of our horses. Find them in our public education repository at github.com/equiphysics/education.