CROSS-REFERENCES
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of and priority to U.S. Provisional Patent Applications No. 63/967,576, No. 63/974,083, No. 63/974,099, No. 63/988,460, No. 63/988,475, No. 63/988,480, No. 63/991,254, and No. 63/991,263. All prior provisional applications are incorporated herein by reference in their entireties.
I.FIELD OF THE INVENTION
[0001]The present disclosure relates generally to computational biology, machine learning, and computer architecture. More specifically, it relates to systems and methods for graphical processing unit (GPU) execution control, numerical stability enforcement, and deterministic execution in the simulation of fixed-compartment tumor history and clonal evolution using continuous-time neural ordinary differential equations (ODEs).
II.BACKGROUND OF THE INVENTION
1.Fixed-Compartment Clonal Evolution Models
[0002]Advanced digital twin platforms utilize foundation models, such as Variational Autoencoders (VAEs), to encode patient multi-omics data into structured latent spaces. These latent representations are subsequently processed by hypernetworks to parameterize continuous tumor trajectories via Neural Ordinary Differential Equations (ODEs). In modeling clonal evolution, tumors are represented as a mixture of distinct sub-clonal populations utilizing a fixed-compartment architecture — a fixed set of compartments representing sensitive and various resistant states. The initial state of these compartments is defined by a clonal fraction vector, which must strictly adhere to simplex constraints (all fractions must be non-negative and sum exactly to one).
2.GPU Stability Challenges
[0003]Executing these complex, continuous-time ODE solvers on GPUs introduces severe technical challenges. If the initial clonal fraction vector violates simplex constraints due to upstream neural network approximations or floating-point inaccuracies, the ODE solver can rapidly accumulate numerical errors, resulting in NaN or Infinite values. When these invalid values propagate into the solver's integration logic, they cause catastrophic runtime failures. Specifically, a NaN value propagating to the ODE solver's adaptive step size can result in an undefined cast to integer during memory indexing, triggering a hardware-level CUDA_ERROR_ILLEGAL_ADDRESS exception that corrupts the CUDA context and crashes the serving stack.
III.SUMMARY OF THE INVENTION
[0004]The invention provides a privilege-separated resistance sentinel architecture that: (1) intercepts inference requests prior to ODE solver dispatch and enforces strict simplex validation on clonal fraction vectors; (2) implements a Resistance Sentinel that preserves clinically critical minor resistant subclones in the fixed-compartment model by automatically promoting below-threshold resistant clones to a dedicated compartment; (3) enforces deterministic ordering of all sorting operations across computational stages to prevent floating-point non-associativity from causing index misalignment; and (4) provides a non-bypassable GPU interlock that structurally prevents dispatch of simulation batches with invalid initial conditions to the GPU.
[0005]The system maps biologically derived clonal populations (from VAF-based deconvolution) into the fixed 4-slot ODE compartment model using a priority-based assignment strategy: Slot 0 (dominant sensitive), Slot 1 (secondary), Slot 2 (drug-resistant sentinel), and Slot 3 (immune-evasive). The resistance sentinel ensures that even minor resistant clones (as low as 1% allelic fraction) are preserved in the simulation, preventing clinically dangerous underestimation of resistance emergence timing.
IV.CLAIMS
20 claims covering the privilege-separated resistance sentinel architecture, simplex validation interlock, deterministic ordering enforcement, GPU dispatch gating, 4-slot clonal compartment mapping, resistance sentinel promotion strategy, and NaN/Inf propagation prevention in continuous-time neural ODE tumor simulators.
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