| 1. |
- Fenstermacher, M.E., et al.
(författare)
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DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy
- 2022
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 0029-5515 .- 1741-4326. ; 62:4
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Tidskriftsartikel (refereegranskat)abstract
- DIII-D physics research addresses critical challenges for the operation of ITER and the next generation of fusion energy devices. This is done through a focus on innovations to provide solutions for high performance long pulse operation, coupled with fundamental plasma physics understanding and model validation, to drive scenario development by integrating high performance core and boundary plasmas. Substantial increases in off-axis current drive efficiency from an innovative top launch system for EC power, and in pressure broadening for Alfven eigenmode control from a co-/counter-I p steerable off-axis neutral beam, all improve the prospects for optimization of future long pulse/steady state high performance tokamak operation. Fundamental studies into the modes that drive the evolution of the pedestal pressure profile and electron vs ion heat flux validate predictive models of pedestal recovery after ELMs. Understanding the physics mechanisms of ELM control and density pumpout by 3D magnetic perturbation fields leads to confident predictions for ITER and future devices. Validated modeling of high-Z shattered pellet injection for disruption mitigation, runaway electron dissipation, and techniques for disruption prediction and avoidance including machine learning, give confidence in handling disruptivity for future devices. For the non-nuclear phase of ITER, two actuators are identified to lower the L-H threshold power in hydrogen plasmas. With this physics understanding and suite of capabilities, a high poloidal beta optimized-core scenario with an internal transport barrier that projects nearly to Q = 10 in ITER at ∼8 MA was coupled to a detached divertor, and a near super H-mode optimized-pedestal scenario with co-I p beam injection was coupled to a radiative divertor. The hybrid core scenario was achieved directly, without the need for anomalous current diffusion, using off-axis current drive actuators. Also, a controller to assess proximity to stability limits and regulate β N in the ITER baseline scenario, based on plasma response to probing 3D fields, was demonstrated. Finally, innovative tokamak operation using a negative triangularity shape showed many attractive features for future pilot plant operation.
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| 2. |
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| 3. |
- Sabbagh, S. A., et al.
(författare)
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Resistive wall stabilized operation in rotating high beta NSTX plasmas
- 2006
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 46:5, s. 635-644
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Tidskriftsartikel (refereegranskat)abstract
- The National Spherical Torus Experiment (NSTX) has demonstrated the advantages of low aspect ratio geometry in accessing high toroidal and normalized plasma beta, and βN ≡ 10 8〈βt〉 aB0/Ip. Experiments have reached βt = 39% and βN = 7.2 through boundary and profile optimization. High βN plasmas can exceed the ideal no-wall stability limit, βNno-wall, for periods much greater than the wall eddy current decay time. Resistive wall mode (RWM) physics is studied to understand mode stabilization in these plasmas. The toroidal mode spectrum of unstable RWMs has been measured with mode number n up to 3. The critical rotation frequency of Bondeson-Chu, Ωcrit = ωA/(4q2), describes well the RWM stability of NSTX plasmas when applied over the entire rotation profile and in conjunction with the ideal stability criterion. Rotation damping and global rotation collapse observed in plasmas exceeding βNno-wall differs from the damping observed during tearing mode activity and can be described qualitatively by drag due to neoclassical toroidal viscosity in the helically perturbed field of an ideal displacement. Resonant field amplification of an applied n = 1 field perturbation has been measured and increases with increasing βN. Equilibria are reconstructed including measured ion and electron pressure, toroidal rotation and flux isotherm constraint in plasmas with core rotation ω/ωA up to 0.48. Peak pressure shifts of 18% of the minor radius from the magnetic axis have been reconstructed.
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| 4. |
- Menard, J.E., et al.
(författare)
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Progress in understanding error-field physics in NSTX spherical torus plasmas
- 2010
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 50:4, s. 045008-
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Tidskriftsartikel (refereegranskat)abstract
- The low-aspect ratio, low magnetic field and wide range of plasma beta of NSTX plasmas provide new insight into the origins and effects of magnetic field errors. An extensive array of magnetic sensors has been used to analyse error fields, to measure error-field amplification and to detect resistive wall modes (RWMs) in real time. The measured normalized error-field threshold for the onset of locked modes shows a linear scaling with plasma density, a weak to inverse dependence on toroidal field and a positive scaling with magnetic shear. These results extrapolate to a favourable error-field threshold for ITER. For these low-beta locked-mode plasmas, perturbed equilibrium calculations find that the plasma response must be included to explain the empirically determined optimal correction of NSTX error fields. In high-beta NSTX plasmas exceeding the n = 1 no-wall stability limit where the RWM is stabilized by plasma rotation, active suppression of n = 1 amplified error fields and the correction of recently discovered intrinsic n = 3 error fields have led to sustained high rotation and record durations free of low-frequency core MHD activity. For sustained rotational stabilization of the n = 1 RWM, both the rotation threshold and the magnitude of the amplification are important. At fixed normalized dissipation, kinetic damping models predict rotation thresholds for RWM stabilization to scale nearly linearly with particle orbit frequency. Studies for NSTX find that orbit frequencies computed in general geometry can deviate significantly from those computed in the high-aspect ratio and circular plasma cross-section limit, and these differences can strongly influence the predicted RWM stability. The measured and predicted RWM stability is found to be very sensitive to the E × B rotation profile near the plasma edge, and the measured critical rotation for the RWM is approximately a factor of two higher than predicted by the MARS-F code using the semi-kinetic damping model.
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| 6. |
- Choi, May Yee, et al.
(författare)
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Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes
- 2023
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Ingår i: Annals of the Rheumatic Diseases. - 0003-4967. ; 82:7, s. 927-936
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Tidskriftsartikel (refereegranskat)abstract
- Objectives A novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes. Methods Demographic, clinical and serological data from 805 patients with SLE obtained within 15 months of diagnosis and at 3-year and 5-year follow-up were included. For each visit, sera were assessed for 29 antinuclear antibodies (ANA) immunofluorescence patterns and 20 autoantibodies. K-means clustering on principal component analysis-transformed longitudinal autoantibody profiles identified discrete phenotypic clusters. One-way analysis of variance compared cluster enrolment demographics and clinical outcomes at 10-year follow-up. Cox proportional hazards model estimated the HR for survival adjusting for age of disease onset. Results Cluster 1 (n=137, high frequency of anti-Smith, anti-U1RNP, AC-5 (large nuclear speckled pattern) and high ANA titres) had the highest cumulative disease activity and immunosuppressants/biologics use at year 10. Cluster 2 (n=376, low anti-double stranded DNA (dsDNA) and ANA titres) had the lowest disease activity, frequency of lupus nephritis and immunosuppressants/biologics use. Cluster 3 (n=80, highest frequency of all five antiphospholipid antibodies) had the highest frequency of seizures and hypocomplementaemia. Cluster 4 (n=212) also had high disease activity and was characterised by multiple autoantibody reactivity including to antihistone, anti-dsDNA, antiribosomal P, anti-Sjögren syndrome antigen A or Ro60, anti-Sjögren syndrome antigen B or La, anti-Ro52/Tripartite Motif Protein 21, antiproliferating cell nuclear antigen and anticentromere B). Clusters 1 (adjusted HR 2.60 (95% CI 1.12 to 6.05), p=0.03) and 3 (adjusted HR 2.87 (95% CI 1.22 to 6.74), p=0.02) had lower survival compared with cluster 2. Conclusion Four discrete SLE patient longitudinal autoantibody clusters were predictive of long-term disease activity, organ involvement, treatment requirements and mortality risk.
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| 7. |
- Johansson, Fredrik, 1988, et al.
(författare)
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Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
- 2022
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Ingår i: Journal of Machine Learning Research. - 1533-7928 .- 1532-4435. ; 23:166, s. 1-50
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Tidskriftsartikel (refereegranskat)abstract
- Practitioners in diverse fields such as healthcare, economics and education are eager to apply machine learning to improve decision making. The cost and impracticality of performing experiments and a recent monumental increase in electronic record keeping has brought attention to the problem of evaluating decisions based on non-experimental observational data. This is the setting of this work. In particular, we study estimation of individual-level potential outcomes and causal effects—such as a single patient’s response to alternative medication—from recorded contexts, decisions and outcomes. We give generalization bounds on the error in estimated outcomes based on distributional distance measures between re-weighted samples of groups receiving different treatments. We provide conditions under which our bounds are tight and show how they relate to results for unsupervised domain adaptation. Led by our theoretical results, we devise algorithms which learn representations and weighting functions that minimize our bounds by regularizing the representation’s induced treatment group distance, and encourage sharing of information between treatment groups. Finally, an experimental evaluation on real and synthetic data shows the value of our proposed representation architecture and regularization scheme.
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| 8. |
- Johansson, Fredrik, 1988, et al.
(författare)
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Learning representations for counterfactual inference
- 2016
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Ingår i: 33rd International Conference on Machine Learning, ICML 2016, New York City, United States; 19 June 2016 through 24 June 2016. - 9781510829008 ; 6, s. 4407-4418
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Konferensbidrag (refereegranskat)abstract
- Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art. © 2016 by the author(s).
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| 9. |
- Johansson, Fredrik, 1988, et al.
(författare)
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Predicting response to tocilizumab monotherapy in rheumatoid arthritis: A real-world data analysis using machine learning
- 2021
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Ingår i: Journal of Rheumatology. - : The Journal of Rheumatology. - 1499-2752 .- 0315-162X. ; 48:9, s. 1364-1370
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Tidskriftsartikel (refereegranskat)abstract
- Objective. Tocilizumab (TCZ) has shown similar efficacy when used as monotherapy as in combination with other treatments for rheumatoid arthritis (RA) in randomized controlled trials (RCTs). We derived a remission prediction score for TCZ monotherapy (TCZm) using RCT data and performed an external validation of the prediction score using real-world data (RWD). Methods. We identified patients in the Corrona RA registry who used TCZm (n = 452), and matched the design and patients from 4 RCTs used in previous work (n = 853). Patients were followed to determine remission status at 24 weeks. We compared the performance of remission prediction models in RWD, first based on variables determined in our prior work in RCTs, and then using an extended variable set, comparing logistic regression and random forest models. We included patients on other biologic disease-modifying antirheumatic drug monotherapies (bDMARDm) to improve prediction. Results. The fraction of patients observed reaching remission on TCZm by their follow-up visit was 12% (n = 53) in RWD vs 15% (n = 127) in RCTs. Discrimination was good in RWD for the risk score developed in RCTs, with area under the receiver-operating characteristic curve (AUROC) of 0.69 (95% CI 0.62-0.75). Fitting the same logistic regression model to all bDMARDm patients in the RWD improved the AUROC on held-out TCZm patients to 0.72 (95% CI 0.63-0.81). Extending the variable set and adding regularization further increased it to 0.76 (95% CI 0.67-0.84). Conclusion. The remission prediction scores, derived in RCTs, discriminated patients in RWD about as well as in RCTs. Discrimination was further improved by retraining models on RWD.
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| 10. |
- Makar, Maggie, et al.
(författare)
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Estimation of bounds on potential outcomes for decision making
- 2020
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Ingår i: 37th International Conference on Machine Learning, ICML 2020. ; PartF168147-9, s. 6617-6627
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Konferensbidrag (refereegranskat)abstract
- Estimation of individual treatment effects is commonly used as the basis for contextual decision making in fields such as healthcare, education, and economics. However, it is often sufficient for the decision maker to have estimates of upper and lower bounds on the potential outcomes of decision alternatives to assess risks and benefits. We show that, in such cases, we can improve sample efficiency by estimating simple functions that bound these outcomes instead of estimating their conditional expectations, which may be complex and hard to estimate. Our analysis highlights a trade-off between the complexity of the learning task and the confidence with which the learned bounds hold. Guided by these findings, we develop an algorithm for learning upper and lower bounds on potential outcomes which optimize an objective function defined by the decision maker, subject to the probability that bounds are violated being small. Using a clinical dataset and a wellknown causality benchmark, we demonstrate that our algorithm outperforms baselines, providing tighter, more reliable bounds.
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