WaveCore: a physics-first prediction engine.
Where conventional ML learns patterns from large datasets, WaveCore builds the governing physics of a system into how it computes. One engine, grounded in first principles, so it generalizes across domains and runs on hardware you already own.
Physics-first, by design
Data-first methods are interpolators: brilliant inside the distribution they were trained on, brittle outside it. Most high-stakes problems are extrapolation problems: novel materials, novel chemistries, novel power systems. That is the physics-first side.
Compute the answer from first principles.
Instead of learning a system’s behavior from massive datasets, WaveCore encodes the governing physics into the computation itself. Lighter hardware, lower energy, and signal that holds up in the novel regimes where data-first models tend to break.
Measured, reproducible results.
A selection of what WaveCore has produced on public, independently reproducible datasets, running on commodity hardware. We report what we can defend and hold the rest for technical review.
Predictions that track reality
On a widely used public materials benchmark, WaveCore predicts a key property (critical temperature) close to the measured value across the range, not just on easy cases.
- An R² of 0.876 means the model accounts for about 88% of the real variation in the property.
- Points hug the diagonal across low and high values, so accuracy holds across the range.
- Produced from composition alone, with no first-principles simulation in the loop.
Honest scope: competitive, not state-of-the-art. A fast triage tool, measured against a defensible public baseline; the method behind it is held under NDA.
Same task, far more signal captured
Given the same public task and the same inputs, WaveCore explains close to four times as much of the real-world variation as a conventional statistical baseline.
- The baseline captures roughly a quarter of the variation; WaveCore captures about 88%.
- It is the same result as the 0.876 figure, expressed as a ratio against a fair reference.
- Same inputs on both sides, so the comparison reflects the approach, not extra data.
Honest scope: the baseline is a reasonable conventional reference, not the strongest possible competitor. We report it to show direction and magnitude, not to claim a record.
Fast enough to fit a research cycle
At about 0.02 seconds to predict one material’s property on the public benchmark, a single commodity board screens over a million candidate materials a day, turning an HPC-queue wait into same-day triage.
- Roughly 50 material-property predictions per second on one board, no GPU cluster required.
- Over 1,000,000 candidate materials per day on hardware you already own.
- Throughput adds up across ordinary machines instead of demanding specialized accelerators.
Honest scope: timing is for the prediction step on the public materials benchmark; end-to-end throughput depends on your data pipeline and hardware.
Warning with time to act
On public NASA battery-aging data, a physics-grounded coherence signal flagged end-of-life well before a conventional variance baseline reached the same threshold.
- 87 to 114 cycles of additional lead time on the cells tested.
- Reads the signals a battery-management system already collects, with no teardown.
- Earlier warning means time to schedule maintenance, grade, or replace before failure.
Honest scope: a limited-cell result and an analytical early-warning signal that informs decisions. It does not replace certified safety testing, and broader-chemistry validation is a roadmap milestone.
How to read these. Explained variance (R²) is the share of real-world variation a model accounts for, where 1.0 is perfect. The first two figures are the same result viewed two ways: on one public materials benchmark, with identical inputs, WaveCore reached R² 0.876 while a conventional statistical baseline reached roughly a quarter of that, hence about four times the explained variance. Each figure is reproducible from public data and measured against a defensible baseline. We describe our accuracy as competitive, not state-of-the-art, on purpose. Broader-chemistry and third-party validation are active roadmap milestones; the methods behind these numbers are protected and disclosed only under NDA.
Strong where novel problems live.
Earlier, interpretable warning.
What we hold ourselves to.
General, not bespoke
The principle holds across scales and domains. We resist solutions that only work for one machine in one room.
Additive, not disruptive
WaveCore improves systems that already exist and runs against telemetry customers already collect. The less it asks you to tear out, the more honest the value.
Measurable, not magical
Every claim is observable against a defensible, public baseline. We describe our accuracy as competitive, not state-of-the-art, on purpose.
Safe by construction
Where we instrument other parties’ facilities, every deployment is engineered passive and read-only, least-privilege, and auditable — mapped to recognized security frameworks as design intent, not sold as a separate product.
However your environment requires.
Why this page shows results, not mechanisms.
What you see here are outcomes and principles, or clearly-labeled illustrations. What we never show is how the engine actually works. That is the work, and it stays patent-pending and trade-secret. This isn’t evasiveness; it’s the same discipline we apply to security: share what builds trust, protect what differentiates.
Qualified technical diligence gets the full methodology and benchmarks under NDA.
Curious how we keep ourselves honest?
Our research posture explains how we test, where we report failure, and what we’re willing to put on the record.