TY - JOUR
TI - Fractional calculus innovations and machine learning-driven advances in thermal science
AU - Zhao Lei
AU - Chen Ya
AU - He Ji-Huan
JN - Thermal Science
PY - 2026
VL - 30
IS - 2
SP - 805
EP - 814
PT - Article
AB - This paper explores fractional calculus innovations and machine learning's  role in advancing thermal science, especially in smart textiles. It first  introduces three key fractional derivatives (Caputo, Riemann-Liouville,  two-scale fractal) for thermal analysis, highlighting their strengths in  capturing non-locality, memory effects, and fractal characteristics. Then,  it details how the two-scale fractal deriva-tive modifies Caputo and  Riemann-Liouville derivatives to better model complex thermal systems in  smart textiles, with simplified forms balancing accuracy and computational  efficiency. Finally, it discusses machine learning's synergy with fractional  calculus, optimizing model parameters, capturing nonlinearities, and  enabling data-driven fractional models, to solve intractable thermal  problems in smart textiles, supporting applications like complex textile  material heat transfer and electronic thermal management in wearable smart  textiles.
DO - 10.2298/TSCI2602805Z
ER -