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AI Architecture › L3 · Twin Modeling & AI
L3 · Twin Modeling & AI

Hard-Constraint PINNs for Boundary Conditions

Kronos bakes boundary conditions into the network architecture so they hold exactly, improving conditioning over soft-penalty PINNs.

THE STACK · click to jumpL7Ecosystem & StrategyL6Experience & VisualizationL5Applications & CopilotsL4OrchestrationL3Twin Modeling & AIL2Data FabricL1Control PlaneL0Foundation▲tlmctl▼L3 · TWIN MODELING & AIThe KRONOS-CTRL digital twin and its predictive shadow.1KRONOS-CTRL Twinlive plant state2GNNscoupled subsystems3PINNsphysics-constrained4Anomaly Ensemblesdrift & fault detection5MPCreceding-horizon control6Predictive Shadowruns seconds aheadMACHINE TIEState estimate descends to L1 control; alerts rise to L4 / L5.KRONOS FUSION ENERGYAI-NATIVE S.M.A.R.T. GENERATORTWIN MODELING & AISHEET 05REV. 2026-08L3 · AI-NATIVE STACK
L3 · Twin Modeling & AI — its place in the stack (left, click any layer) and its internal components (right). Telemetry rises; control descends.

Soft vs hard constraints

A standard PINN enforces boundary conditions with a penalty term, so they hold only approximately and compete with the interior residual during optimization. For equilibrium problems where the boundary flux value anchors the whole solution, an imperfect boundary is a real error source. Kronos uses hard-constraint parameterizations that satisfy the boundary by construction.

python
# hard-constrained ansatz: BC satisfied exactly for any network N
#   psi(x) = g(x) + D(x) * N_theta(x)
#   g(x) : a function meeting the boundary data (psi = psi_bnd on dOmega)
#   D(x) : a distance-like function, D = 0 exactly on dOmega
# => on the boundary psi = g = psi_bnd, regardless of N_theta

With this ansatz the boundary term disappears from the loss, leaving only the physics residual (and any data term). The optimization is better conditioned because it no longer trades boundary accuracy against interior accuracy, and training converges faster, which matters when Kronos retrains PINNs in batch as the design evolves pre-FOAK.

Where Kronos applies it

Enforcing symmetry as a hard constraint is especially valuable: the breeder is axisymmetric and the burner mirror is symmetric about its midplane, so encoding these directly halves the effective domain and removes a class of spurious asymmetric solutions the network could otherwise drift into.

Hard constraints do not remove the need for validation; they remove one error mode. The interior physics residual is still checked against reference solvers, and the network's output is still confidence-scored before the twin uses it.

Content reviewed August 2026 · design-and-simulation stage