Core Infrastructure
HorizonPredict — Predictive Science Platform
HorizonPredict
Research and engineering teams face three challenges when building predictive models: **Computational Bottleneck of Traditional Simulation**: High-fidelity physics-based models (CFD, FEA, molecular dynamics) provide accurate predictions but require hours or days per simulation. Exploring design spaces, optimizing parameters, or running real-time predictions becomes computationally infeasible. You're limited to analyzing dozens of scenarios when you need to evaluate thousands. **Data-Driven Models Lack Physical Consistency**: Machine learning models trained on experimental or simulation data can make predictions quickly, but they're black boxes that violate known physical laws and fail unpredictably when extrapolating beyond training data. For scientific applications requiring explainability and reliability, pure ML approaches aren't trustworthy. **Uncertainty Quantification Gaps**: Both traditional simulation and ML models produce point predictions without quantifying confidence. When making design decisions or operational choices, you need to know not just what the model predicts, but how certain that prediction is. Current tools provide predictions without the uncertainty bounds necessary for risk-informed decisions.
Who This Is For
**Research Directors** leading teams generating vast simulation or experimental datasets who need faster insights without sacrificing accuracy. **Principal Scientists** who need to explore parameter spaces comprehensively but face computational limits with traditional simulation. **Engineering Leaders** requiring real-time predictions for design optimization, process control, or operational decision-making in fields where pure simulation is too slow. This is for organizations in aerospace, automotive, energy, materials, pharmaceuticals, climate science, or advanced manufacturing where computational modeling is central to R&D. If simulation queue times limit your research velocity or you can't answer urgent engineering questions within available timeframes, hybrid predictive modeling becomes essential.
What You Get
HorizonPredict delivers a complete hybrid modeling platform that combines physics-based simulation with machine learning to achieve both accuracy and speed. You get models that run 100-1000x faster than traditional simulation while respecting physical laws and providing quantified uncertainty bounds. Your research and engineering teams can explore parameter spaces comprehensively, optimize designs through thousands of iterations, and make real-time predictions in operational settings—all while maintaining the scientific rigor required for critical decisions.
How We Work
Key Deliverables
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Hybrid Physics-ML Model Architecture
Integrated modeling framework combining approaches:
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Domain-Specific Predictive Models
Custom models tailored to your scientific problem:
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Uncertainty Quantification Framework
Rigorous confidence estimation:
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High-Throughput Parameter Exploration
Accelerated design space analysis:
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Real-Time Prediction Inference
Production-ready deployment for operational use:
6
Automated Model Training Pipeline
Continuous improvement infrastructure:
7
Integration with Simulation Tools
Seamless connection to your existing computational infrastructure:
8
Explainability & Interpretability Tools
Understanding what models learn:
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Benchmark Dataset & Validation Suite
Rigorous testing infrastructure:
10
Knowledge Transfer & Training
Ensuring your team can maintain and extend the platform: