The next wave of robotics depends on unifying code and hardware—embedding AI directly into the deterministic systems that ...

Recover a smooth 1-D target from 32 noisy observations: u(x) = sin(π x), x ∈ [-1, 1], y = u(x) + ε, ε ~ N(0, σ²), σ = 0.05. Because the body is frozen at random init, the effective "feature map" is a ...

* ``num_chains=1`` — particle-cloud **mean** as the warm position.

As an experienced researcher in Prognostics (science of prediction) in machinery health I would like to leave a brief note to newcomers and already progressed researchers about the field. I always ...

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Abstract: Due to the capability of the physics-informed neural network (PINN) to solve complex partial differential equations automatically, it has revolutionized the field of scientific computing.

Abstract: PINNs are neural networks that undergo training to execute supervised learning tasks while adhering to certain laws of physics, which are typically defined by basic nonlinear partial ...

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PINNs, on the other hand, embed governing equations (like Navier-Stokes) directly into the loss function. This means they don’t just fit data; they respect the laws of physics, even with sparse or ...