A Convex Quasilinearization Method for Solving Nonlinear PDEs with Physics-Informed Neural Networks
Researchers propose a convex optimization framework that sidesteps the nonconvex training bottleneck endemic to physics-informed neural networks. By applying Bellman-Kalaba quasilinearization, the method decomposes nonlinear PDEs into sequences of linear subproblems solved via direct least-squares factorization rather than gradient descent. The approach maintains PINN expressiveness through Linear-in-Learnables trial spaces, including random features and spectral bases, while replacing stochastic optimization with deterministic convex solves. This addresses a fundamental pain point in scientific ML: training instability and local minima that plague standard PINNs on complex nonlinear systems.
Modelwire context
ExplainerThe deeper shift here is architectural philosophy: rather than improving gradient-based training, this work argues the optimization problem itself should be restructured so that neural network training never becomes nonconvex in the first place. That is a different bet than most PINN improvement work, which accepts nonconvexity and tries to manage it.
The connection to 'Kolmogorov Regression for Robust Diffusion Policies' from the same day is worth noting: both papers respond to instability in learned systems by replacing stochastic optimization with deterministic PDE-grounded formulations. That parallel suggests a broader methodological current in scientific ML, where researchers are increasingly skeptical that gradient descent is the right primitive for physics-constrained problems. The diffusion policy paper reformulates score matching as a boundary-value problem; this paper replaces backprop with sequential least-squares. Neither claims the other's territory, but together they point toward a growing preference for deterministic structure over stochastic approximation in settings where physical laws constrain the solution space.
The critical test is whether the quasilinearization sequence converges reliably on PDEs with strong nonlinearities like turbulent Navier-Stokes or nonlinear Schrodinger regimes. If the authors or independent groups publish benchmarks on those cases within the next six months, that will clarify whether this is a general solver or a method that works cleanly only on well-behaved nonlinearities.
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MentionsPhysics-Informed Neural Networks (PINNs) · Bellman-Kalaba quasilinearization · Linear-in-Learnables (LiL) · extreme learning machines · QR factorization
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