Achieving sustained thermonuclear fusion in tokamak reactors [1, 2] requires a precise understanding and control of burning plasma behavior, particularly under the high-power deuterium-tritium (D-T) conditions anticipated in ITER [3-5]. In these plasmas, interactions among energetic fusion alpha particles, electrons, and ions give rise to complex nonlinear processes, including collisional heating, radiative losses, impurity effects, and multi-region energy transport. These dynamics are strongly influenced by both global and local plasma parameters, such as magnetic field strength, safety factor, impurity concentration, and transport coefficients. Quantifying the sensitivity of plasma behavior to these parameters is essential for robust scenario design, performance optimization, and predictive control of fusion reactors. Previous studies [4, 6-10] have proposed various models to simulate burning plasma dynamics. However, many of these approaches rely on empirical scaling laws with limited flexibility across operational regimes. To address these challenges, NeuralPlasmaODE [11, 12] was developed as a data-informed, multi-region, multi-timescale modeling framework based on neural ordinary differential equations (Neural ODEs) [13, 14]. This framework builds upon prior nodal modeling of tokamak plasmas [10, 15-18] and has demonstrated strong performance in capturing energy transport and species interactions in both DIII-D and ITER scenarios. In this work, we extend NeuralPlasmaODE [11, 12] to conduct a comprehensive sensitivity analysis of transport and radiation effects in ITER burning plasmas.

Hall Effect Thrusters (HETs) are electric thrusters that eject heavy ionized gas particles from the spacecraft to generate thrust. Although traditionally they were used for station keeping, recently They have been used for interplanetary space missions due to their high delta-V potential and their operational longevity in contrast to other thrusters, e.g., chemical. However, the operation of HETs involves complex processes such as ionization of gases, strong magnetic fields, and complicated solar panel power supply interactions. Therefore, their operation is extremely difficult to model thus necessitating Data Assimilation (DA) approaches for estimating and predicting their operational states. Because HET's operating environment is often noisy with non-Gaussian sources, this significantly limits applicable DA tools. We describe a topological approach for data assimilation that bypasses these limitations that does not depend on the noise model, and utilize it to forecast spatiotemporal plume field states of HETs. Our approach is a generalization of the Topological Approach for Data Assimilation (TADA) method that allows including different forecast functions. We show how TADA can be combined with the Long Short-Term Memory network for accurate forecasting. We then apply our approach to high-fidelity Hall Effect Thruster (HET) simulation data from the Air Force Research Laboratory (AFRL) rocket propulsion division where we demonstrate the forecast resiliency of TADA on noise contaminated, high-dimensional data.

A non-linear complex system governed by multi-spatial and multi-temporal physics scales cannot be fully understood with a single diagnostic, as each provides only a partial view and much information is lost during data extraction. Combining multiple diagnostics also results in imperfect projections of the system's physics. By identifying hidden inter-correlations between diagnostics, we can leverage mutual support to fill in these gaps, but uncovering these inter-correlations analytically is too complex. We introduce a groundbreaking machine learning methodology to address this issue. Our multimodal approach generates super resolution data encompassing multiple physics phenomena, capturing detailed structural evolution and responses to perturbations previously unobservable. This methodology addresses a critical problem in fusion plasmas: the Edge Localized Mode (ELM), a plasma instability that can severely damage reactor walls. One method to stabilize ELM is using resonant magnetic perturbation to trigger magnetic islands. However, low spatial and temporal resolution of measurements limits the analysis of these magnetic islands due to their small size, rapid dynamics, and complex interactions within the plasma. With super-resolution diagnostics, we can experimentally verify theoretical models of magnetic islands for the first time, providing unprecedented insights into their role in ELM stabilization. This advancement aids in developing effective ELM suppression strategies for future fusion reactors like ITER and has broader applications, potentially revolutionizing diagnostics in fields such as astronomy, astrophysics, and medical imaging.