Abstract: The present disclosure relates to systems, methods, and products for optimization of a chromatography purification process using a physics-informed neural network. The method includes inputting a plurality of process parameters into the physics-informed neural network to obtain a predicted output; calculating a loss function based on a set of governing equations, as set of constraints, and the predicted output; determining whether the physics-informed neural network is convergent based on the calculated loss function; in response to the physics-informed neural network being convergent, exporting the physics-informed neural network; and in response to the physics-informed neural network not being convergent: updating a plurality of weights in the physics-informed neural network, and inputting the plurality of process parameters to the physics-informed neural network for a next convergence iteration to calculate the loss function and determine whether the physics-informed neural network is convergent.

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.