Dinesh Kumar

Employment

• German Aerospace Center
• University of Bristol

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (56)

• Preference adaptive multi-objective HVAC control using a vectorized deep transformer Q-network
  Control Engineering Practice 2026 DOI: 10.1016/j.conengprac.2026.106886
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• Adaptive Transformer Q-Networks for Energy and Comfort Optimization in Smart Buildings
  Lecture Notes in Computer Science 2026 DOI: 10.1007/978-3-032-19137-3_20
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• An integrated CPFE–PCE framework for efficient uncertainty quantification of texture-dependent plasticity in 316L stainless steel
  Mechanics of Materials 2026 DOI: 10.1016/j.mechmat.2026.105725
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• AI-driven uncertainty quantification & multi-physics approach to evaluate cladding materials in a microreactor
  Progress in Nuclear Energy 2025 DOI: 10.1016/j.pnucene.2025.105793
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• AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology
  Progress in Nuclear Energy 2024 DOI: 10.1016/j.pnucene.2024.105177
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• IMPACT OF TEXTURE ON MECHANICAL PROPERTIES OF STAINLESS STEEL (SS316) MATERIAL USING A POLYNOMIAL CHAOS-BASED SURROGATE MODEL WITH CRYSTAL PLASTICITY SIMULATIONS
  American Society of Mechanical Engineers Pressure Vessels and Piping Division Publication PVP 2024 DOI: 10.1115/PVP2024-123284
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• Machine Learning and Artificial Intelligence-Driven Multi-scale Modeling for High Burnup Accident-Tolerant Fuels for Light Water-Based SMR Applications
  Handbook of Smart Energy Systems Volume 1 4 2023 DOI: 10.1007/978-3-030-97940-9_149
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• Surrogate Modeling-Driven Physics-Informed Multi-fidelity Kriging for the Prediction of Accident-Tolerant Fuel Properties
  Handbook of Smart Energy Systems 2023 DOI: 10.1007/978-3-030-97940-9_204
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• Surrogate Modeling-Driven Physics-Informed Multi-fidelity Kriging for the Prediction of Accident-Tolerant Fuel Properties
  Handbook of Smart Energy Systems Volume 1 4 2023 DOI: 10.1007/978-3-030-97940-9_204
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• Recent advances in uncertainty quantification methods for engineering problems
  AI Assurance 2023 DOI: 10.1016/b978-0-32-391919-7.00027-5
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