Physics-Informed Neural Networks

Universal mathematical primitives for audio, visual, and memory systems. Solve PDEs 100x faster with physics-guided machine learning.

Get Started → ▶ Try Demo
53
PINN Benchmarks
4
Domain Adapters

Core Capabilities

A unified mathematical framework bridging quantum physics, machine learning, and practical applications.

📊

Saturation Modeling

Michaelis-Menten inspired capacity limits with soft-knee compression. Perfect for dynamic range control and signal processing.

🎯

Quality Scoring

Normalized prediction accuracy equivalent to R². Works across audio, visual, and numerical domains.

📄

Phase Mismatch Index

Unified coherence metric combining drift, quality, and frequency deviation for system health monitoring.

🚀

Drift Detection

Real-time out-of-distribution detection in embedding space. Catch model degradation before it impacts production.

⚡

Size Throttling

Adaptive output control based on capacity, quality, and coherence. Automatic scaling for optimal performance.

🧠

PINN Training

53 benchmark problems across fluid dynamics, quantum systems, and structural mechanics with auto-tuning.

Simple API

Integrate physics-informed metrics in minutes. Works with any ML framework.

import richmond_sdk as rf # Authenticate rf.authenticate("rf_dev_your_key") # Calculate quality score q = rf.quality_score( predictions=[1.1, 2.0, 2.9], ground_truth=[1, 2, 3] ) # → 0.942 # Full system assessment result = rf.full_assessment( predictions=preds, ground_truth=truth, embedding_current=z_now, embedding_reference=z_ref, pressure=0.5, saturation_threshold=1.0 ) print(result["status"]) # → "healthy"

REST API

Or call directly from any language via our REST endpoints.

# Full assessment endpoint curl -X POST https://api.richmond.ai/v1/assess \ -H "X-API-Key: rf_pro_your_key" \ -d '{ "predictions": [1.1, 2.0, 2.9], "ground_truth": [1, 2, 3], "z_current": [...], "z_reference": [...], "P": 0.5, "P_sat": 1.0 }' # Response: { "quality_score": 0.942, "pmi": 0.158, "throttle": 0.67, "status": "healthy" }

Simple Pricing

Start free, scale as you grow. No hidden fees.

🎉 Founder Pricing - 90% Off Year 1!

Year 1: 90% off • Year 2: 50% off • Year 3+: Standard pricing

Free
£0

For learning and experimentation

  • 4 starter benchmarks
  • View Richmond Index scores
  • 50 API calls/day
  • Documentation access
Get Started Free
Tinkerer
£99/year
£10/year
Launch price

For researchers and enthusiasts

  • All 25+ basic benchmarks
  • Auto-Tune optimization
  • 4 custom benchmarks
  • 5 model saves
  • 500 API calls/day
Subscribe - £10/yr
Professional
£299/year
£30/year
Launch price

Full access for professionals

  • All 50+ benchmarks (incl. advanced)
  • Auto-Fix engine
  • 10 custom benchmarks
  • 25 model saves
  • 2,000 API calls/day
Subscribe - £30/yr
Enterprise
£599/year
£60/year
Launch price

For power users and teams

  • Everything in Professional
  • Custom PDE definitions
  • 20 custom benchmarks
  • 100 model saves
  • 10,000 API calls/day
Subscribe - £60/yr

Built For

Cross-domain applications powered by unified physics principles.

🎵

Audio Processing

RAVE integration, phase vocoders, spectral analysis

🖼️

Visual AI

Diffusion models, NeRF, video consistency

🧠

Memory Systems

Attention gates, cache management, DNCs

📈

Quantitative Finance

Signal generation, risk metrics, position sizing

Ready to Get Started?

Join thousands of developers using physics-informed primitives to build better ML systems.

Contact Us →