در این پژوهش، مروری جامع بر کاربرد مدل های یادگیری ماشین نظارت شده در پیش بینی و بهینه سازی عملکرد سیستم های ذخیره سازی حرارت نهان با استفاده از مواد تغییر فاز انجام شده است. هدف اصلی مطالعه، شناسایی مدل های پرکاربرد، مقایسه دقت آن ها و بررسی نوآوری های طراحی هندسی و بهبود انتقال حرارت در این سیستم ها می باشد. نتایج نشان داد که شبکه های عصبی مصنوعی و عمیق به عنوان محبوب ترین مدل ها اغلب در ترکیب با الگوریتم های تکاملی نظیر الگوریتم ژنتیک و بهینه سازی ازدحام ذرات برای بهینه سازی همزمان به کار گرفته شده اند. سایر مدل های مؤثر شامل جنگل تصادفی و رگرسیون بردار پشتیبان برای داده های محدود و مدل های تقویتی (XGBoost و LightGBM) برای بهبود دقت و تفسیرپذیری می باشند. همچنین، رگرسیون فرایند گاوسی با قابلیت تخمین عدم قطعیت در برخی مطالعات به کار رفته است. این مرور بیان می کند که یادگیری ماشین توانسته محدودیت های مدل سازی دینامیک سیالات محاسباتی را جبران کرده و روابط پیچیده میان پارامترهای هندسی و عملیاتی را با دقت بالا مدل سازی نماید. در نهایت، استفاده از مدل های ترکیبی و رویکردهای چندهدفه، راهکار مؤثری برای بهینه سازی سیستم های ذخیره سازی حرارت نهان مبتنی بر مواد تغییر فاز خواهد بود.
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