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The Job Shop Scheduling (JSS) problem can be viewed as an integer optimization program with linear objective function and linear, disjunctive constraints. The Lagrangian-based deep learning model does not necessarily produce feasible schedules directly. The model presented below is used to construct solutions that are integral, and feasible to the original problem constraints. The experimental setting, as defined by the training and test data, simulates a situation in which some component of a manufacturing system'slows down', causing processing times to extend on The model training follows the selection of parameters presented in Table 3.Parameter V alue Parameter V alue Epochs 500 Batch Size 16 Learning rate [1 . Finally, Constraints (23) capture Kirchho ff's Current Law and Constraints (24) capture Ohm's Law.

This optimal solution is traditionally found by solving a convex optimization problem.

Scientists and engineers have been working on approximating the governing PDEs of these physical systems for centuries. The emergence of the computer-aided simulation facilitates a cost-friendly way to study these challenging problems.

To demonstrate this, we performed an experiment.

These observations suggest the existence of a tradeoff between average robustness and robust fairness. We use var and rob.acc to denote the variance of class-wise robust accuracy, and average robust accuracy, respectively. We use var and rob.acc to denote the variance of class-wise robust accuracy, and average robust accuracy, respectively. B.1 Naturally Trained Linear model We use var and rob.acc to denote the variance of class-wise robust accuracy, and average robust accuracy, respectively. For any classifier f ( x) in Equation ( 2), we first calculate its natural risk.

The largest and most varied such publicly available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5 m/pixel, empowered by European Space Agency's (ESA) Phi-Lab as part of the ESA-funded