The simple search analyzer is a custom Picturepark implementation not using Elastic search defaults. The custom analyzer uses a regex:

• Regex
  */"(\[^\\p\{L\}\\d\]+)|(?<=\\D)(?=\\d)|(?<=\\d)(?=\\D)|(?<=\[\\p\{L\}&&\[^\\p\{Lu\}\]\])(?=\\p\{Lu\})|(?<=\\p\{Lu\})(?=\\p\{Lu\}\[\\p\{L\}&&\[^\\p\{Lu\}\]\])"/*
• Outcome:
  • Lowercase / Uppercase
  • Digit / non-digit
  • Stemming
  • HTML Strip
• Examples
  • Picturepark = Picturepark, picturepark
  • Case Study = Case, Study, case, study

If you want to test the simple search analyzer, you can check your terms in a regex tester to see the outcome.

1. Open a regex checker
   1. regex101: build, test, and debug regex
   2. RegExr: Learn, Build, & Test RegEx
2. Add your term as a test string
3. Check the outcome

The no diacritics analyzer:

• only works for text fields
• strip diacritic characters, so when the text value is: Kovačić Mateo you can search for “Kovačić Mateo” or “Kovacic Mateo”.

An example can be found in Elastic Search Documentation: ASCII folding token filter | Elasticsearch Guide [8.17] | Elastic

The path hierarchy analyzer will:

• Take a path found in a field (picturepark\platform\manual) and delimit the individual terms
• Example
  • picturepark\platform\manual = picturepark\platform\manual, picturepark\platform, manual
  • Products/Family/Industry = Products/Family, Products, Products/Family/Industry

You should only configure this analyzer if being used via API. The simple search in Picturepark escapes Special Characters, and therefore you won't find assets when searching for some of the tokens generated by this analyzer. 
An example can be found in Elastic Search Documentation: Path hierarchy tokenizer | Elasticsearch Guide [8.17] | Elastic

There are several language analyzers available for elastic search. Language analyzers prevent stemming from language-specific values and language-specific stopwords. 
Language analyzers | Elasticsearch Guide [8.17] | Elastic 
The current implementation is using the default Elastic Search Language analyzers as listed in the link. We are using the default stop words and rules for stemming, without any custom adaption.

Starting point for exact substring matches was ngram tokenizing, which indexes all the substrings up to length n. The drawback of ngram tokenizing is a large amount of disk space used. 
Best practice:

• Use ngram only if required - use carefully and not for every string

Settings allow to define min and max grams created on indexing and token_chars, which are characters classes to keep in the tokens, Elasticsearch splits on characters that don't belong to any of these classes. 
Example: Search "Raven"

• NGrams (splits term into tokens with one character):
• Rav
• Rave
• Raven
• ave
• aven
• Ven
• ...

Example: Search "Pegasus"

• NGrams (splits term into tokens with one character):
• Pegasus
• Degas

Examples are in Elastic Search Documentation:
N-gram tokenizer | Elasticsearch Guide [8.17] | Elastic

This tokenizer is very similar to nGram but only keeps n-grams that start at the beginning of a token. Settings allow to define min and max grams created on indexing and token_chars, which are characters classes to keep in the tokens, Elasticsearch splits on characters that don't belong to any of these classes.

Examples are in Elastic Search Documentation:
Edge n-gram tokenizer | Elasticsearch Guide [8.17] | Elastic

Simple Analyzer: Simple analyzer | Elasticsearch Guide [8.17] | Elastic

No Diacritics: ASCII folding token filter | Elasticsearch Guide [8.17] | Elastic

Path Hierarchy: Path hierarchy tokenizer | Elasticsearch Guide [8.17] | Elastic

Language: Language analyzers | Elasticsearch Guide [8.17] | Elastic

NGram: N-gram tokenizer | Elasticsearch Guide [8.17] | Elastic

EdgeNgram: Edge n-gram tokenizer | Elasticsearch Guide [7.6] | Elastic