DNAI Whitepapers
Deep-dive technical documentation on the architecture, algorithms, and validated performance of the Physics-Constrained Digital Twin platform.
Foundation White Paper
Neuro-Symbolic architecture overview. H-BDVAE perception engine, dual-path system, and physics-constrained simulation.
The Dark Matter of Oncology
Why genomics alone cannot predict resistance. Non-Mutational Resistance and the failure of standard AI.
Sim-to-Real Architecture
Split-Source Transfer Learning for predictive oncology. From organoids to patients via domain separation.
Patent Application
Physics-Constrained Sim-to-Real Transfer Learning in Computational Oncology.
Patent 2: Representational Collapse Prevention
Preventing metabolic scaling-induced representational collapse in cross-species oncology models.
Patent 3: Uncertainty & Missing Modalities
Uncertainty-calibrated missing modality handling, separated-state dynamics, and multi-layer UQ.
Patent 4: Ontology-Guided Autogradient
Preventing training collapse in domain adaptation via ontology-guided autogradient modulation.
Patent 5: Adjoint Sensitivity Control
Control of adjoint sensitivity computation graphs and physics-constrained gradient topologies.
Patent 6: Distributionally Robust Training
DRO training of physics-constrained cancer digital twins using dual-mode risk set construction.
Patent 7: Risk-Averse Stochastic Optimization
Stabilized stochastic inference and risk-averse optimization in physics-constrained oncology digital twins.
Patent 8: Runtime Resource Gating
Cryptographically enforced runtime resource gating in ODE solvers via memory allocation interlock.
Patent Deep-Dive
A presentation and AI-generated podcast walking through the patents and the physics behind the platform.
Physics-Constrained Cancer Dynamics
15-slide visual walkthrough of the sim-to-real architecture, separated-state ODEs, and patent portfolio.
Using Physics to Translate Mouse Cancer
AI-generated podcast discussion covering the patent innovations, cross-species transfer learning, and clinical implications.
Hybrid Engine Performance
Validated on TCGA Pan-Cancer (33 cancer types) + PDX Models
Ready to see DNAI in action?
Schedule a demo to see how physics-constrained simulation can transform your oncology workflows.