1 INTRODUCTION

2 DIFFERENT COMPONENTS OF ICE IN POLAR REGION

3 PHYSICAL MODEL FOR POLAR ICE

4 DATA-DRIVEN MACHINE LEARNING FOR POLAR ICE

5 METHODOLOGY FOR INCORPORATING PHYSICAL LAW WITH MACHINE LEARNING TECHNIQUES

6 CASE STUDY: PHYSICS-INFORMED MACHINE LEARNING ON ICE

7 ADVANTAGES OF PHYSICS-INFORMED MACHINE LEARNING ON STUDYING POLAR ICE

8 CHALLENGES AND OPPORTUNITY

9 CONCLUSION AND FUTURE OUTLOOK