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• Novel machine learning algorithm based on physics informed neural networks (PINNs) for approximating solutions of forward and inverse problems for radiative transfer. • Fast, robust and accurate approach, independent of the equation dimensionality. • Estimates on the generalization error of PINNs for the unsteady and steady forward problem. • Extensive numerical experiments demonstrating the accuracy and efficiency of the proposed algorithm. We propose a novel machine learning algorithm for simulating radiative transfer. Our algorithm is based on physics informed neural networks (PINNs), which are trained by minimizing the residual of the underlying radiative transfer equations. We present extensive experiments and theoretical error estimates to demonstrate that PINNs provide a very easy to implement, fast, robust and accurate method for simulating radiative transfer. We also present a PINN based algorithm for simulating inverse problems for radiative transfer efficiently. [ABSTRACT FROM AUTHOR]

We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems. This algorithm is based on deep neural networks and its key feature is the iterative selection of training data through a feedback loop between deep neural networks and any underlying standard optimization algorithm. Numerical examples for optimal control, parameter identification and shape optimization problems for PDEs are provided to demonstrate that ISMO significantly outperforms a standard deep neural network based surrogate optimization algorithm as well as standard optimization algorithms. [ABSTRACT FROM AUTHOR]