AI4SC HUB — Platform
Capabilities

Every tool your scientific
ML team actually needs

Four concrete engineering bottlenecks, addressed by a single integrated platform. From first CUDA kernel to production deployment.

01 / 04

High-performance CUDA & Triton kernel development

Custom operator development for non-standard sparse matrix operations, physics-informed layers, and non-local boundary conditions — with memory coalescing analysis and stride verification before any runtime error surfaces.

  • Memory coalescing efficiency scoring with per-warp analysis
  • Shared memory usage optimisation and bank conflict detection
  • Tensor stride mismatch detection for pybind11-bound tensors
  • Occupancy calculator with register pressure recommendations
  • Triton script profiling for custom sparse operator kernels
  • Warp divergence identification with remediation suggestions
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ai4sc.profiler — sparse_physics_kernel — A100 SXM4
Kernel analysis complete · 5 metrics · 2 warnings
coalescing_eff
94.2%
✓
shared_mem
47.8 KB
✓
occupancy
87.5%
✓
warp_diverge
2.1%
⚠
register_press
64 regs
⚠
⚠ Stride mismatch at line 127 — align to 128-byte boundaries
→ Estimated speedup after fix: 2.3×
Generated fix: sparse_physics_kernel_v2.cu — review pending
02 / 04

Physics-informed neural operator architecture

Fourier Neural Operators, DeepONets, and PDE-constrained loss engines with automatic gradient stability checks, clean Pydantic-validated interfaces, and FP16/BF16 mixed-precision support.

  • Fourier Neural Operator (FNO) with configurable spectral modes
  • Deep Operator Networks (DeepONets) for solution operator learning
  • PDE-constrained loss engines: L_data + L_pde + L_bc
  • Mixed-precision (FP16/BF16) with automatic stability assertions
  • GNN-based operators for irregular and unstructured grids
  • Physics-Informed Neural Networks (PINN) baseline implementations
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ai4sc.operators — FNO architecture diagram
Fourier Neural Operator · G_θ: a(x) → u(x)
Input a(x)
→
Lifting layer
→
v₀ ∈ ℝᵈ
↓ × L Fourier layers
ℱ spectral conv
+
W linear conv
→
σ(v_l+1)
↓ Projection
Output u*(x)
Loss: ℒ = λ₁‖u_θ - u_data‖² + λ₂‖𝒩(u_θ) - f‖² + λ₃‖u_θ|_∂Ω - g‖²
✓ Gradient stable BF16 verified Pydantic config
03 / 04

Numerical stability & gradient verification

Trace compute graphs, analyse custom VJP/JVP backward passes, and verify loss formulations to eliminate NaN explosions and gradient leakage across long temporal rollouts in mixed-precision training.

  • Custom VJP/JVP backward-pass analysis for JAX and PyTorch
  • Gradient norm monitoring with per-layer breakdown charts
  • NaN and Inf origin tracing through the complete compute graph
  • FP16/BF16 mixed-precision stability validation suite
  • Long temporal rollout stability assertions with configurable bounds
  • Gradient leakage detection through custom autodiff layers
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ai4sc.stability — gradient norm by layer (FNO, BF16)
0 1 2 NaN L1 L2 L3 L4 L5 L6 L7 After AI4SC fix (stable) Before (unstable)
VJP analysis: gradient leakage in L3 custom autodiff layer — patched in 1 session
04 / 04

Automated CI/CD benchmarking & drift detection

Scientific code requires more than unit tests. AI4SC HUB generates property-based tests, automated benchmark suites, and CI workflows that catch numerical drift before it reaches a reviewer — every release, automatically.

  • GitHub Actions workflow generation from existing test suites
  • Numerical drift detection per release with configurable tolerances
  • Hypothesis property-based tests for physics operator invariants
  • pytest-benchmark suites with baseline tracking across commits
  • API documentation generated from docstrings and type annotations
  • Developer onboarding guides auto-generated from interface definitions
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ai4sc-ci · main · run #142 · aong-ml/surrogate-core
Triggered by push to main · 7 steps · 1m 44s
✓
Build CUDA extensions (pybind11)
42s
✓
pytest — unit tests (142 passed)
8.4s
✓
Hypothesis — physics operator invariants
24.1s
✓
pytest-benchmark — FNO, DeepONet, PINN
18.7s
✓
Numerical drift check (±0.001 tolerance)
3.2s
✓
Auto-generate API docs from docstrings
6.8s
✓
Publish benchmark comparison report
1.4s
✓ All checks passed · No numerical drift detected · Docs updated · Benchmarks within baseline ±2%
At a Glance

Capability availability by plan

Capability Free Pro Enterprise
CUDA kernel profiling Basic metrics Advanced Full suite
Gradient / VJP analysis — Included Included
FNO / DeepONet implementations Community Production-grade Custom
CI/CD benchmark generation — Included Included
Numerical drift CI guards — Included Included
On-premise / air-gapped deployment — — Included
Custom kernel development support — — Dedicated team
View detailed pricing
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