{'fraction_non_alphanumeric': 0.021229050279329607, 'fraction_numerical': 0.0022346368715083797, 'mean_word_length': 5.686567164179104, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Real-Time Estimation of Discharge Current Oscillations Using an Iterated Extended Kalman Filter

Computational efforts to simulate plasma processes have routinely faced challenges due to the multiscale, multispecies, and multidimensional nature of plasma behavior. Work towards predictive modeling of these processes spans from kinetic simulations that track particle-level interactions to fluid-based models for device scale phenomena. 1 , 2 Yet, the physics-based models remain challenging when the physics becomes more complex (cf. turbulence). Advancements in data-driven modeling and machine learning provide new opportunities for predictive modeling capabilities. The benefits of these approaches include the ability to use experimental data to solve the inverse problem and uncover the time histories of non-observed quantities that allows for real-time estimation of the plasma dynamics.