Abstract: The present disclosure relates to systems, methods, and products for adaptive design and optimization using a physics-informed neural network (PINN). The system includes a non-transitory memory and a processor. The processor executes instructions to cause the system to: input collocation points and design parameters into the PINN to obtain an output; calculate a loss function based on a set of governing equations and the output; determine whether the PINN is convergent based on the calculated loss function; in response to the PINN being convergent, export the PINN; and in response to the PINN not being convergent: determine whether to resample the collocation points; determine an optimum number of collocation points; determine a set of optimal network parameters for adjusting the PINN; and input the collocation points and the set of optimal network parameters to the PINN for a next iteration.